Cost Efficient Provisioning of Wireless Access

Transcription

1 Cost Efficient Provisioning of Wireless Access Infrastructure Cost Modeling and Multi-Operator Resource Sharing KLAS JOHANSSON Licentiate Thesis in Telecommunication Stockholm, Sweden 2005

2

3 Cost Efficient Provisioning of Wireless Access Infrastructure Cost Modeling and Multi-Operator Resource Sharing KLAS JOHANSSON Licentiate Thesis in Telecommunication Stockholm, Sweden 2005

4 TRITA S3 RST 0520 ISSN ISRN KTH/RST/R--05/20--SE KTH Signaler, sensorer och system SE Stockholm SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till offentlig granskning för avläggande av teknologie licentiatexamen fredagen den 16 december 2005 klockan 14:00 i sal C1, Electrum, Kungliga Tekniska Högskolan, Isafjordsgatan 22, Kista. c Klas Johansson, december 2005 Tryck: Universitetsservice US-AB

5 Abstract A cost efficient design of radio access networks is crucial for a continued growth of mobile data services. In this thesis we study two opportunities that operators have to lower their infrastructure costs; adapting network deployment to local variations in traffic demand and multi-operator resource sharing. With an increasing number of radio access technologies available, finding the proper mix of systems is a critical issue for the operators. For this purpose, we propose a model for estimating the total infrastructure cost as a function of average traffic density. The main input parameters are the average cost, throughput, and range per base station. Based on a log-normally distributed, spatially correlated, traffic density map the network is dimensioned for a given set of base station types. With this network cost model, we illustrate how the cost depends on average traffic density for different single and multi-access network concepts. Moreover, we identify how the respective subsystem in a multi-access network should be improved in order to most effectively cut network costs. Mobile infrastructure costs can also be reduced through network sharing between multiple operators, and this has lately been put in focus during the deployment of the third generation s mobile systems. With a joint radio access network, problems may arise in terms of free-rider effects, and there is a risk for consciously misleading traffic forecasts with the objective to hide marketing plans for competitors. This motivates a fair radio resource allocation between sharing operators, in particular for cellular systems where over-dimensioning is quite expensive. To avoid a reservation of radio resources, which decreases average capacity utilization, we propose a load based priority queuing as an alternative solution to this problem. Even without preemption of connected users, we show that blocking levels can be sustained for operators with less than agreed load. This comes, however, at the cost of increased call setup times during congestion. Furthermore, roaming between overlapping mobile networks could be exploited to increase user data rates, in particular at the cell border. By means of simulations with three similar cellular overlaid networks, we quantify the gain with national roaming for an urban scenario. The gains are, thanks to increased diversity against shadow fading, significant already with almost co-located base stations. iii

6

7 Acknowledgements Half way through the doctoral studies, I would like to take this opportunity to express my gratitude towards a number of persons who have contributed to this work. To start with, the collaboration with Anders Furuskär, Johan Hultell, and my supervisor Professor Jens Zander, as well as the previous work with Martin Kristensson at Nokia Networks has been very rewarding. I could not wish for a more professional and creative atmosphere! Furthermore, the feedback from, and interesting discussions with, Magnus Almgren, Bo Karlson, Peter Karlsson, Perttu Laakso, Jonas Lind, Professor Gerald Maguire, Jan Markendahl, Guest Professor Östen Mäkitalo, Mats Nilson, Professor Bertil Thorngren, and Jan Werding have been very valuable. A special thanks goes to Christian Bergljung for taking on the role as external reviewer at the licentiate seminar, Martin who reviewed the licentiate thesis proposal, and to Anders, Bo, Johan, Martin, and Östen for commenting on the thesis draft. The daily work with courses, teaching, etc., would probably not be endurable without the class mates within the Graduate School of Telecommunications and the colleagues at the Radio Communication Systems Lab (Pietro Lungaro and Bogdan Timus, to just mention a few). The administrative support from Niklas Olsson, Irina Radulescu, and Lise-Lotte Wahlberg is also very much appreciated. I would also like to thank all my friends and former colleagues who, in one way or another, inspired me to initiate this education and cheer along the way of this mental marathon. In particular, David Astèly, Christian Braun, Ann-Louise Johansson, Mats Larsson, Professor Preben Mogensen, and Klaus Pedersen from the research department of Nokia Networks, who taught me the engineering basics and for sharing their enthusiasm for radio communications. I also very much appreciate all activities with friends from the Electrical Engineering program at KTH, skiing, and radio broadcasting, which to an equally large extent contribute to my personal development. Above all, though, I am very grateful and happy to have my beloved Maria, my parents Britta and Lars, and my sister Jenny. Their love and support is truly admirable. v

8

9 Contents I 1 1 Introduction Background Scope of the Thesis Thesis Outline Infrastructure Cost Modeling Related Work Contributions Delimitations Base Station Characteristics (Paper 1) Heterogeneous Infrastructure Cost (Paper 2) Cost Efficient Capacity Expansion (Paper 3) User Deployed Access Points (Paper 4) Conclusions Multi-Operator Resource Sharing Related Work Contribution Fair Resource Sharing (Paper 5) Throughput with National Roaming (Paper 6) Conclusions Concluding Remarks Summary Future Work References 59 Appendices 67 vii

10 viii Contents A Cost Modeling and Pricing Strategies 69 A.1 Discounted Cash Flow Modeling A.2 Pricing Strategies B Network Sharing Use Cases 73 B.1 Mobile Virtual Network Operators B.2 Inter-Operator Charging with Roaming Based Sharing II Paper Reprints 77 5 Base Station Characteristics (Paper 1) 79 6 Heterogeneous Infrastructure Cost (Paper 2) 87 7 Cost Efficient Capacity Expansion (Paper 3) 95 8 User Deployed Access Points (Paper 4) Fair Resource Sharing (Paper 5) Throughput with National Roaming (Paper 6) 117

11 List of Tables 2.1 Single carrier WCDMA BS performance assumptions Cost and performance estimates for different technologies Summary of base station densities and infrastructure costs System parameters used in national roaming simulations ix

12

13 List of Figures 2.1 Approximative macro cell ranges at different carrier frequencies Cost structure of cellular BSs Cost per area unit covered with WCDMA (uniform traffic) Example of a generated traffic map and network deployment Cost for different combinations of radio access technologies Cost for a multi-access network with different macro cell radii Elasticity of infrastructure cost in a multi-access network Fraction of traffic covered with user deployed APs Cost for various mixes of user and operator deployed APs Principle network sharing methods Admission control with non-preemptive priority queuing Probability of blocked calls per operator An illustration of the inter-operator site distance model Expected gain in user throughput gain with national roaming.. 52 xi

14

15 List of Abbreviations 1G,..., 4G First,..., Fourth Generation of Mobile Systems 3GPP Third Generation Partnership Program AP Access Point ARPU Average Revenue Per User BS Base Station CAPEX Capital Expenditures CDMA Code Division Multiple Access DCH Dedicated Channel DSL Digital Subscriber Line GSM Global System for Mobile Communications HSDPA High Speed Downlink Packet Access IEEE Institute of Electrical and Electronics Engineers IEEE a/b/g WLAN standards authorized by IEEE MNO Mobile Network Operator MVNO Mobile Virtual Network Operator O&M Operation and Maintenance OPEX Operational Expenditures QoS Quality of Service RAN Radio Access Network RRM Radio Resource Management S3G Super 3G (Long Term Evolution of 3G) SIR Signal to Interference (plus noise) Ratio SLA Service Level Agreement UMTS Universal Mobile Telephony System WCDMA Wideband Code Division Multiple Access WLAN Wireless Local Area Network xiii

16

17 Part I 1

18

19 Chapter 1 Introduction In this thesis we treat two topics of relevance for a cost efficient capacity expansion of mobile data networks. More specifically: 1. How mobile network operators (MNO) could exploit various radio access technologies in order to cut infrastructure costs. 2. The specific technical problems and possibilities that arise when multiple operators (service providers) share the same radio access network (RAN). Next we will outline the background to the study and a few underlying assumptions, and thereafter define and motivate the general problem addressed and the thesis scope. More detailed problem descriptions and a review of previous related work are included in Chapter 2 and 3 respectively, which also summarize the included papers. 1.1 Background Evolution of the Mobile Internet The first generation of mobile systems (1G) was launched in the beginning of the 1980s. A decade later, service offerings were exclusively targeted towards business users and the service penetration rate was low. However, after the introduction of second generation systems (2G), prices declined during the second half of the 1990s and mobile telephony was surprisingly soon adopted by most people in the developed countries [1]. At the same time, the Internet, with services like web-browsing, file downloads, and , changed people s way of living and doing business. The success of the Internet was enabled by Moore s law, resulting in continuously increasing computer processing and memory capabilities, 3

20 4 Chapter 1. Introduction the fact that packet switched transmission in principle allow users to always be connected without allocating expensive network resources, and an open network architecture which basically allowed anyone to provide interesting services and applications. Today the majority of the population in industrialized countries is connected and penetration rates for residential access have more than doubled since the millennium shift [2, 3]. In the mid 1990s, the business cases of mobile telephony and the Internet merged into a common vision the Mobile Internet. The business logic seemed obvious; mobile users could access a tremendous amount of useful and entertaining Internet based services wherever they where. This would clearly open up for new revenue streams and the Mobile Internet was incredibly hyped. However, while predictions at that time suggested that the average data traffic volumes would reach 150MB per user in January 2004 [4], voice services are still prevailing in most countries. Demand for Wireless Access The demand for telecommunications in general, and mobile telephony in particular, is largely driven by the value of having the opportunity to communicate, and network effects 1 [6]. To a large extent, these factors explain the demand for coverage and international roaming in mobile networks, as well as inter-connection of networks. It furthermore partly motivates the importance for telecom operators to have a large subscriber base; having that, they can capture parts of the value that network effects brings [6]. Wireless Local Area Networks (WLAN), on the other hand, are today mainly used for lap-tops and handhelds tailored for data services and the user behavior is essentially the same as with fixed broadband, or local area networks. That is, Internet-access, file transfers, etc., at homes and in offices which benefit from a short transmission delay (per message, file, etc.). We can thus observe that while area coverage and mobility have been important for telecommunication services, a high data rate is often valued more for data oriented services. The Cost of Coverage and Mobility In spite of the increasing number of radio access technologies with various characteristics available [7], systems that are capable of providing Internet connectivity 1 By network effects, we include both that a network is worth more if more services and people are accessible (Metcalfe s law [5]), but also that an established communication per se will lead to more communication.

21 1.1. Background 5 with wide area coverage and support for mobile usage are still quite expensive. Mobile telephony, on the other hand, has evidently been a good stroke of business and the demand for coverage and mobility has driven the mobile systems towards base stations (BS) with high output power and highly mounted antennas, sophisticated mechanisms for mobility management and fast handovers between cells, and complex billing systems supporting various pricing strategies (see further Appendix A.2). As a consequence, entry barriers have been high in the wireless infrastructure market, and the cost of switching supplier is an important criteria when an MNO purchase new infrastructure. A key enabler for the success of mobile telephony has been the availability of spectrum. Since voice and messaging services (personal communications) only require a low average and peak data rate per session, a relatively small bandwidth of radio spectrum is sufficient. Regulators have therefore, so far, been able to put spectrum in suitable frequency bands at the operators disposal. 2 In conjunction with the low data rate requirements per link, the maximum feasible range per BS has also been quite high (in the order of 10 30km in 1G and 2G). Hence, relatively few BSs were required for (almost) full area coverage and today we have nationwide coverage for mobile telephony in most developed countries. The situation is quite different for services requiring higher peak and mean data rate per session. From the supply perspective, higher data rates imply both higher bandwidth requirements, which have been difficult to find in lower bands, and shorter feasible communication distances. This fact complicates spectrum assignment procedures and requires denser networks (which might be quite expensive). Already in third generation systems (3G), targeted for data rates in the order of 100kbps with full area coverage, cell radii in the order of a few hundred meters are required to obtain good indoor coverage in urban environments. Hence, significantly higher data rates (that is, > 1Mbps) are most likely economically feasible only in specific places, like malls and large enterprizes, and not in general. Spectrum Regulation Spectrum allocation procedures have varied considerably between countries and services, and is an intricate question from both a technical, societal, and business 2 Both the carrier frequency and bandwidth strongly affect what combination of area coverage and data rate per link that can be served with a BS. Propagation path gain decrease as a function of carrier frequency and, according to the Shannon bound, (R = W log 2 (1 + SIR)), data rates R increase linearly with system bandwidth W, but only logarithmical with the signal to interference ratio (SIR).

22 6 Chapter 1. Introduction perspective [6, 8]. Radio spectra have traditionally been reserved for specific services and technologies, and concessions are handed out (primarily) to broadcasting companies, military organizations, and telecom operators. This paradigm is currently being challenged, and with an increasing number of analogue radio systems being replaced by digital successors, large portions of spectrum is at stake also in lower frequency bands. These bands are very attractive for a plethora of mobile services, including broadcasting, personal communications, and internet access. Parts of the radio spectrum have also been allocated to low-power transmitters, which do not require a license (for example, the bands used for WLAN and other short range technologies). Despite that the range is poor at high frequencies, it is most often sufficient for indoor systems. Moreover, an advantage with high carrier frequency for indoor BSs is that the channel can be reused already in adjacent buildings and floors, and there is, as oppose to for mobile systems, no particular need for exclusive spectrum use rights. The high value of radio spectrum adequate for mobile services was evident in the the recent 3G license auctions in Europe. To use auctions or not, and how to design the auction process, has been discussed for more than forty years [6,9]. Typically only a few (3 5) MNOs are currently targeted per market to ensure that reasonable quantities are available at affordable prices, while allowing operators to cover their fixed costs [6,10]. However, to keep license fees at reasonable levels (avoiding the winner s curse phenomenon 3 ), it has been stressed that at least one more license than in previous systems has to be awarded when new systems are introduced [6]. Since the spectrum allocation process becomes increasingly complex, especially considering the multitude of systems and services available, alternative solutions are currently being investigated. Instead of long term allocations, spectrum is envisaged to be assigned more dynamically for different services and technologies, and with finer granularity; see for example [12, 13]. Inter-connection Regulation Both in the copper-line access network and mobile networks, regulators try to stimulate competition by enforcing the owner of the access networks to allow for independent (virtual) operators to resell access subscriptions. In particular for residential broadband subscriptions, the legacy telephony networks have proven to be sufficient for the majority of consumers, and the low-cost Digital Subscriber Lines (DSL) have clearly boosted the residential broadband penetration in many countries [3]. As demand takes off, DSL operators can then expand their business gradually and eventually roll-out their own fiber networks instead of facing huge investments upfront for the last mile access network. 3 Meaning that the winner of an auction realizes that the object was more worth for him than the other participants, and thus overbid [11].

23 1.2. Scope of the Thesis 7 Price regulation is frequently applied for inter-connection charges between operators in order to avoid that licensees and incumbent fixed-line operators set inter-connection charges too far above the production cost [6, 14, 15]. There is, however, a risk that firms fail to recover sunk costs 4 with cost based price regulation whereby this needs needs to be done carefully. 1.2 Scope of the Thesis General Problem Description Mobile data services are widely believed to bring great opportunities for the society, just as the fixed Internet and mobile telephony already have. And, as a matter of fact, research and development towards future wireless infrastructure is today essentially driven by a desire to increase data rates in order to support a greater variety of services [16, 17]. However, as outlined above, there are still many issues that need to be solved in order to attract the average consumer. Besides innovation and marketing of new services that fulfill user needs, we can presume that the cost of production needs be reduced significantly (especially where user density is low). Whereas steadily increasing data rates, thanks to the diminishing cost of electronics, have been feasible to offer at a flat rate in fixed broadband networks, 5 it is questionable if the same will hold true also for mobile systems. This since denser networks ultimately are required if significantly higher data rates are to be provided, which is associated with high costs for site buildout, rental, etc. [18]. Alternatives to building yet denser cellular networks are therefore important, and that is the point of departure of this thesis. Research Approach The cost of providing mobile data services can be reduced in several ways. If we limit the scope to the RAN, which typically constitutes the bulk of the infrastructure cost, an MNO could in principle choose to: Improve the physical layer transmission techniques. Exploit service requirements and propagation channel characteristics in radio resource management (RRM). Automate the network planning and optimization processes. Acquire more radio spectrum in sufficiently low frequency bands. 4 Sunk costs have traditionally been high for telecom operators, both for mobile networks and fixed line telephony infrastructure. 5 Once the cabling is in place, data rates in fixed networks can readily be increased by upgrading routers, switches, etc. That is, electronics, for which the cost diminishes according to Moore s law.

24 8 Chapter 1. Introduction Adapt BS capabilities to local coverage and capacity requirements using multi-access networks or hierarchical cell structures. Form a network sharing agreement with other operators in areas where the own customer base is to small to cover the fixed costs. Whereas the three first methods have been the traditional focus of technical researchers in radio communication systems, the other are rather related to non-technical issues; such as traffic demand, regulations, competitive strategy, and financial considerations. Even though we acknowledge the importance of improving the technical performance of different systems and the value of adequate spectrum allocations, we will focus on the two latter methods. This in order to investigate to what extent these solutions, which are in the borderland between technology and business, are useful as a means to lower infrastructure costs for mobile data networks. Throughout the thesis the focus will be on low and moderately high data rates (up to approximately a few hundred kbps per user). This is motivated by the reasoning presented in Section 1.1. That is, that only moderate data rates are economically viable to provide with wide area coverage in mobile networks. 6 Contributions More specifically, we will analyze the infrastructure cost of wireless networks adapted to a geographically varying traffic load, and multi-operator resource sharing. Brief problem statements and a summary of the contributions within the respective research topic are provided next. Infrastructure Cost Modeling Operators can choose to deploy specific cellular indoor solutions and WLAN to provide coverage for high data rates in specific places. This typically requires a high willingness to pay per user, and is today most common in airports, hotels, etc. Traditionally, though, hierarchical cell structures have also been used as capacity fill-in in zones with high traffic density. However, with WLAN technology available, operators also have the option to deploy multi-access networks instead of hierarchical cell structures if traffic demand increases significantly. While it is widely accepted that future networks will consist of a blend of radio access technologies [19 22], less is known about what cost savings that can be expected with such heterogeneous wireless networks. The primary objective 6 For some specific places and services, for instance fixed wireless access, higher data rates could still be worthwhile to offer, but such deployments are outside the scope of this thesis.

25 1.2. Scope of the Thesis 9 herein is therefore to quantify to what extent an operator can lower its infrastructure costs by utilizing BSs with different characteristics to cover a non-uniform spatial traffic density. In particular, we will compare the cost of using WLAN access points in traffic hot spots with a conventional (single-access) hierarchical cell structure. Moreover, the need for improved cellular systems and alternative, user deployed, expansion strategies will also be considered. For this purpose an infrastructure cost model has been developed, which has been used to evaluate different combinations of single and multi-access systems in the following conference contributions (which are summarized in Chapter 2): 1. Klas Johansson, Anders Furuskär, Peter Karlsson, and Jens Zander, Relation between base station characteristics and cost structure in cellular systems, In Proc. PIMRC 2004 [23]. 2. Anders Furuskär, Klas Johansson, and Magnus Almgren, An Infrastructure Cost Evaluation of Single- and Multi-Access Networks with Heterogeneous Traffic Density, In Proc. VTC2005 Spring [24]. 3. Klas Johansson and Anders Furuskär, Cost efficient capacity expansion strategies using multi-access networks, In Proc. VTC2005 Spring [25]. 4. Klas Johansson, On the cost efficiency of user deployed access points integrated in mobile networks, In Proc. RVK 2005 [26]. The author of the thesis was main responsible for the first, third, and fourth paper. Anders Furuskär contributed with significant parts of the simulation models and was the primary author of the second paper. All modeling and the research approach has been developed jointly by the author and Anders Furuskär. Peter Karlsson contributed with valuable comments and ideas in particular on the first paper. Magnus Almgren assisted with the initial research approach and network dimensioning principles used in the three last papers. Jens Zander has as advisor been involved and provided valuable feedback and guidance in all papers. Multi-Operator Resource Sharing Network sharing has recently been put into practice by some 3G operators and the first operational networks were recently deployed in Sweden [27 29]. 7 Technically, multiple operators access the same RAN using, to a large extent, mechanisms originally designed for international roaming. In a more general sense, roaming based network sharing also includes Mobile Virtual Network Operators (MVNO). National roaming between geographically overlapping cellular networks could also be exploited by operators to reduce their risk exposure when introducing new services. 7 Rudimentary forms of network sharing, such as site and antenna sharing were widely used also in 2G; see further Appendix B for a summary of network sharing methods.

26 10 Chapter 1. Introduction The advantages with network sharing are promising, especially when considering the new business possibilities for MVNOs (see further Appendix B.1) and that mobile data services could become economically viable also in less populated areas. However, there are considerable drawbacks in terms of reduced differentiation possibilities as well as administrative and technical overhead. Hence, the transaction costs may be significant, in particular for incumbent operators [27 31], which will be discussed further in Section 3.1. We have studied technical solutions to one specific problem that has been raised; namely how network resources can be allocated in a fair way between operators sharing a cellular network. We will also investigate to what extent coverage for higher data rates can be increased by means of national roaming. This in order to further examine what the long-term use case of network sharing might be for mobile network operators. These two problems were addressed in the following two papers (summarized in Chapter 3): 5. Klas Johansson, Martin Kristensson, and Uwe Schwarz, Radio Resource Management in Roaming Based Multi-Operator WCDMA networks, In Proc. VTC2004 Spring [32]. 6. Johan Hultell and Klas Johansson, An Estimation of the Achievable User Throughput with National Roaming, Submitted to VTC2006 Spring [33]. The author of the thesis was main contributor to the first paper, for which Martin Kristensson, Uwe Schwarz, and also Preben Mogensen contributed with valuable feedback, both on the initial ideas and concept, and in identifying use cases for fair radio resource sharing between operators. The second paper was joint work with Johan Hultell where both contributed to an equal extent in all aspects. Jens Zander provided feedback and guidance for both of the papers. Other Related Papers The following papers are related to, but not included, in the thesis and discuss multi-operator RRM respectively business models for user deployed access points (AP) at a conceptual level. Johan Hultell, Klas Johansson, and Jan Markendahl, Business models and resource management for shared wireless networks, In Proc. VTC2004 Fall [31]. Klas Johansson, Jan Markendahl, and Per Zetterberg, Relaying access points and related business models for low cost mobile systems, In Proc. Austin Mobility Roundtable, 2004 [34]. Klas Johansson, Jonas Lind, Miguel Berg, Johan Hultell, Niklas Kviselius, Jan Markendahl, and Mikael Prytz, Integrating User Deployed Local Access Points in a Mobile Operator s Network, In Proc. WWRF meeting #12, 2004 [35].

27 1.3. Thesis Outline Thesis Outline The thesis consists of two parts. The first part contains a presentation of the results and discussion of the included papers of the respective topic in Chapter 2 and Chapter 3. The thesis is summarized and recommendations for future work are outlined in Chapter 4. In the second part, consisting of Chapter 5 10, the series of papers that constitute the contributions of this thesis are reprinted in verbatim. We have also included a brief overview of commonly used cost terminology, pricing strategies, and network sharing use cases in Appendix A B.

28

29 Chapter 2 Infrastructure Cost Modeling In this chapter, we will analyze to what extent the incremental cost associated with increasing traffic volumes can be reduced by adapting network capacity and base station capabilities to local variations in demand. More specifically, we will compare multi-access networks with conventional hierarchical cell structures. Whereas the former refers to systems where multiple radio access technologies (cellular and WLAN) are accessed with a multi-radio capable terminal, the latter describes a single-access network constituted of various cell sizes (macro, micro, etc.). As compared to hierarchical cell structures, multi-access networks in essence have the benefit that other (simpler) protocol stacks and larger (unlicensed) spectrum bandwidths can be used in local access systems (WLAN), yielding cheaper systems and higher data rates. The advantage with hierarchical cell structures, on the other hand, is that handsets and central systems only need to support one access method. It is well known that considerable cost savings can be obtained by adapting radio access systems to the data rates, capacity, and degree of coverage required. For example, a system deployed in rural areas need a long range, but relatively few users are served per BS. Consequently, a smaller chunk of spectrum in lower frequency band is preferred as compared to a wide bandwidth at a higher frequency; see further Figure 2.1. Down-town areas with heavy traffic, on the other hand, seldom require long range so the requirements on technology are the opposite. 1 If traffic is uniformly distributed within a service area, the network design is, from an engineering perspective, straightforward since one technology essentially minimizes cost for a given scenario. With a heterogeneous spatial traffic density, 1 In suburbs and urban areas with low-rise buildings, which in many European countries cover the largest part of the population, BSs with medium range and capacity are typically most cost efficient. 13

30 14 Chapter 2. Infrastructure Cost Modeling Uplink user throughput (outdoors) [Mbps] GHz 2GHz 3GHz Distance from base station [m] Figure 2.1: A rough estimation of user throughput as a function of cell range for a few carrier frequencies in a noise limited system. The COST231-Hata model for outdoor, urban, propagation has been used (with parameters according to [25]) and throughput is calculated using the Shannon-bound, with 3.84MHz carrier bandwidth. The figure illustrates how valuable spectrum in lower bands is for services requiring area coverage. though, introducing a mix of different BSs and radio access technologies with varying characteristics could potentially reduce costs. In this chapter we will therefore examine if there are any cost advantages to take local variations in traffic load into account when deploying a heterogeneous wireless network. A case study with a few common radio access technologies will be performed. The focus in on infrastructure; even though terminals constitute a significant cost for mobile operators and users, we assume that especially data and multi-media handsets need to be multi-mode anyway to support legacy mobile systems and private WLANs. 2.1 Related Work Previous work on the cost structure of wireless access networks has mainly been conducted during the development of the Universal Mobile Telephone System (UMTS) [1, 10, 18]. National regulators have further on developed cost models for regulation of inter-connection charges and spectrum assignment guidelines [10,14,15,36,37]. Technology road-maps and visions, supported by economical reasoning, have also been presented by telecom equipment vendors [4, 38],

31 2.1. Related Work 15 and technical researchers have to some extent used infrastructure cost as objective function for optimization of network dimensioning [39 44]. All of these studies cover both useful methodology and empirical data on unit costs, and are summarized next. Business case feasibility studies Infrastructure cost models have previously been applied in investment analyses for wireless access provisioning. That is, the objective of these studies is to evaluate whether or not introducing a new communication technology is profitable. Significant contributions have been made within the European Unions research programs RACE II (Research of Advanced Communication Technologies), ACTS (Advanced Communication Technologies and Services), and the recently finished TONIC (Techno-Economics of IP Optimized Networks and Services) project. In particular, a methodology for techno-economical evaluation of access networks for telecommunication services was developed during in the TITAN project (Tool for Introduction Scenario and Techno-economic Evaluation of Access Network), as part of RACE II [45]. This was later on refined within ACTS, which finished in year 2000, under the acronym TERA (Techno-economic results from ACTS), and in TONIC [1]. While RACE II and ACTS subsequently led to the development of network architectures and radio access technologies for UMTS, TONIC was mainly carried out during the downturn in the telecom sector, shortly after the millenniumshift. With the widely debated license fees and high investments for 3G networks [6, 46], the aim was to quantify the long-term profitability for UMTS operators. WLAN hot spots were also addressed, since it rapidly had emerged as a competing technology to UMTS. A number of typical scenarios were evaluated, including small and large European countries and various operator sizes. The main results of TONIC were presented in terms of net present values (and internal rate of return), and demand was modeled as a function of time using Scurves [1]. The pay-back time was estimated to be approximately seven years, which was not considered to be too long having in mind that the concessions typically have a duration of 20 years. Clearly this fact will be a significant entry barrier for alternative technologies that now try to enter the mobile market [7]. Following up on TONIC, the CELTIC initiative recently launched ECOSYS (techno-economics of integrated communication SYStems and services). The aim with that project is to provide insights into risk management for new market actors providing fixed and mobile Internet access and services, in an increasingly heterogeneous market for wireless infrastructure. Among their early contributions is an overview of demand forecasts for fixed and mobile networks and services in Europe [3]. This includes an overview of the market share of different broadband technologies, and their distinguishing characteristics, showing that residential broadband penetration currently increase exponentially within Western Europe (with Sweden in the forefront).

32 16 Chapter 2. Infrastructure Cost Modeling Similar methodology has been used to analyze the profitability of Swedish UMTS licensees [46]. It was concluded that the profits of Swedish operators would decrease with UMTS, in principle due to larger investments and operational expenditures (OPEX) at sustained average revenue per user (ARPU). This was, however, based on the operators initial license applications, which promised a significant UMTS coverage also in the rural parts of Sweden. At the moment, however, the Swedish regulator and operators are considering alternative technologies in the 450MHz band (which still is used for 1G) to cover less populated areas. A case study of the economical feasibility of public WLAN deployed in a large scale in Stockholm was presented in [47]. It was shown that there is a potential for pure WLAN operators that provide limited coverage and mobility in public places, such as museums, bus stops, inner city squares, etc. The users willingness to pay has to exceed approximately 50SEK per month in order to cover the costs for infrastructure. However, the author pointed out that revenues need to be considerably higher for the business case to be considered as profitable for a private actor due to the high risk. An option, proposed in [47], would instead be that the public sector should deploy a public WLAN network in selected places provide access, possibly free of charge, until demand increase and private actors find it profitable to offer public WLAN access. Network planning and optimization Improving the link budget and the throughput per BS is as previously mentioned widely accepted as an effective method to increase cost efficiency in mobile networks. The financial benefits with a few capacity enhancement techniques were analyzed in [38, 39]. For instance in [38], a net present value analysis was presented for the case of introducing advanced antenna concepts in 3G systems. However, no additional costs were associated with the capacity enhancing features. The problem of optimizing BS range and aggregate throughput versus cost was treated in [40, 41, 48]. All these studies have the infrastructure cost as objective function for network optimization. For example, in [41], an optimum power allocation and placement of BSs with respect to infrastructure cost was considered. Under the assumption of uniformly distributed traffic load and a target blocking probability the optimization problem was shown be convex, and thus a global optimum can be found. The cost of a BS was modeled as a linear function of the composite output power and it was observed that minimizing the number of BSs, which was done in for example [49,50], not necessarily minimize overall infrastructure costs considering the lower cost for smaller BS types. Moreover, the cost structure shift from BSs and physical infrastructure to last mile -transmission as BS size decreases and consequently, as noted in [41], lowering cost for the actual BS itself does not necessarily lower total infrastructure cost.

33 2.1. Related Work 17 In wireless networks the fixed part of the last mile -transmission stand for a significant portion of the overall infrastructure costs in the order of 5-15% [1, 44]. Optimization of the topology and aggregation points (hubs in star-shaped networks) has been quite extensively studied; see for example [1, 41] and references therein. Methods to lower BS site costs (in total) were also discussed in [42], where it was noted that pedestrian users do not need as advanced BSs as vehicular do. Moreover, cost reductions of 60-70% per subscriber were estimated by introducing a micro-cell layer in second generation mobile systems. Automated solutions are also often sought in order to reduce both investments and running costs by a proper cell planning and network management. An overview of the potential cost reduction with different automation solutions was presented in [43]. Automatic network optimization techniques are popular since they do not require additional hardware (or at least very little). However, it was also noted in [43] that an improved physical layer also is required if an order of magnitude higher capacity is needed. Residential broadband access The aforementioned European research projects also covered techno-economical feasibility of fixed broadband networks. Already within RACE, the TITAN project developed methodology and tools for analyzing the net present value and assess risks associated with providing broadband services [51]. It was argued in [51] that the most critical parameters to include in a technoeconomical model for broadband systems are subscriber density, civil works configuration, component cost evolution, and demand assessment (service penetration). The copper network was showed to be the cheapest solution. In rural areas, however, wireless access (also called radio in the local loop ) was significantly cheaper than fixed line alternatives. Component prices were too high at that time (1996) for fiber based solutions to be considered profitable. However, it was stressed that fiber based infrastructure will be increasingly interesting for fixed broadband access, in particular considering the expected lower operation and maintenance (O&M) costs and the potential to offer more services. It is clear that the incremental cost for installing fiber is significantly higher than DSL. The cost per fiber line (residence) was at the time of these studies about US$ , whereof approximately US$1500 is for the passive optical network [52]. This should be compared to the incremental cost for a DSL connection, which is less than a tenth of that [53]. Wireless fixed broadband systems have recently appeared in the market, especially for rural markets. Although it has been available for 5-10 years, a wider interest for these solutions occurred only recently. The reason for this is probably the growing broadband market as a whole, including digital subscriber line (DSL) systems, a political interest to make broadband available for every citizen, and an increasing competition amongst radio access technologies and

34 18 Chapter 2. Infrastructure Cost Modeling operators. There are already a number of systems available, including the time division duplex mode of WCDMA, IEEE a (WiMAX) and early fourth generation (4G) system concepts. A techno-economical feasibility study of IEEE a was presented in [53], based on the methodology and tools developed within ACTS and TONIC. The results showed that the cost structure of fixed wireless access can not compete with fixed DSL in cities and suburbs due to high equipment prices and low range of the systems, especially in the currently available 3.5GHz frequency band. This was also concluded in [7]. However, for rural areas and regions with inferior infrastructure the fixed broadband wireless access systems look more promising and as compared to mobile systems the link budget is significantly better thanks to that directional roof-top antennas can be used (as with terrestrial TV-broadcasting). Assessments of Spectrum Allocations Having access to radio spectrum is, as discussed in Chapter 1, indeed a fundamental issue for MNOs. This topic has also been much debated during recent years, and there are several contributions on the topic. The value of spectrum for UMTS operators was assessed in a consultancy report commissioned by the Swedish regulator Post och Telestyrelsen in 2004 [36]. This study also included estimates of the infrastructure cost, in order to quantify the economical benefits of allocating additional spectrum for Swedish UMTS operators. It was concluded that the cost of BSs constitute a large part of the total cost of the networks, and that minimizing the number of BSs therefore is important. For more densely populated areas, capital expenditures (CAPEX) could be reduced in the order of 2-6 billion SEK if more spectrum can be added instead of new sites (one BS costing approximately 1 million SEK). Savings in OPEX was assumed to be less significant, in the order of 5% of CAPEX. However, since the need for additional capacity occurs in the future, the present value is lower (800 MSEK). This study does not take technology advances into account and due to the difficulty of making traffic forecasts it was concluded that the point of time when additional spectrum is needed is difficult to predict. A thorough cost analysis was also presented in a report for the Federal Communications Commission in 1992, as part of a quite extensive study assessing the spectrum required for personal communication services [10] 2. A case study was conducted for a hypothetical residential area of households which was to be provided personal communication services. The focus was on potential economical synergies between telephony services, cable TV and mobile telephony. The long-run average cost per subscriber was used as performance measure and recommendations were made regarding suitable spectrum allocations and number of licenses to award for personal communication services in the United States. Different cell sizes were used in the cellular network depending on spectrum allocation, leading to varying infrastructure costs per subscriber. Interesting to 2 This report was later summarized in a journal article [54].

35 2.2. Contributions 19 observe is that many of the conclusions and concepts in that study still are very relevant, although empirical cost and performance estimates differ from present levels. User deployed access points User deployed infrastructure has also been proposed as a method to reduce the cost of wireless access infrastructure [55 57]. In [56] the APs are anticipated to be owned by private users who connect them to their existing broadband connections. Access is then granted for everyone, free of charge, or possibly on a reciprocal basis for those that also have local access networks. Another common case of user deployment concerns APs deployed by local operators having roaming agreements with public operators [58]. Large scale deployment of WLAN was treated in [59, 60] where it, among other things, was noted that WLAN dimensioning in fact is a 3-dimensional problem, and introduced a systematic approach for planning of such systems. In [57], a case study of random AP deployment in offices, shopping malls and campus areas was conducted. It was concluded that the number of APs required for full coverage roughly could be halved with a planned network instead of randomly deployed APs. 2.2 Contributions Previous research suggests that infrastructure costs can be lowered through BSs that are well adapted to the deployment case. However, neither a framework for analyzing the cost structure of heterogeneous networks, nor any quantification of achievable cost savings, have been found in the literature. In Paper 1 4, which are reprinted in Part II (Chapters 5 8), we have therefore investigated the cost of using heterogeneous networks to expand the capacity in a mobile network. Furthermore, we evaluate how the respective radio access technologies should be developed most effectively as part of a multi-access network. The main contributions of the respective paper are as follows: 1. A methodology is derived for average infrastructure cost modeling under uniform traffic densities, and the cost structure is derived for a few common cellular BS configurations. 2. The average cost model is extended to the case of non-uniform spatial traffic densities, in order to assess the cost of heterogeneous networks. Numerical examples are also included with a number of different present and future radio access technologies. 3. It is shown how the proposed cost model can be used to find the proper tradeoff between macro cell radius and AP density, for a mixed 3G and WLAN network. We also introduce the elasticity of infrastructure cost as

36 20 Chapter 2. Infrastructure Cost Modeling an effective method to compare achievable cost reductions with respect to improvements in both unit cost and performance. 4. Potential cost savings with user deployed APs integrated in mobile networks are estimated with a random, uniformly distributed, placement of APs. 2.3 Delimitations When assessing the cost structure of a mobile network, a great number of factors need to be considered. Proper delimitations and simplifications are therefore necessary. Network dimensioning is a function of Demand user data rates, area coverage, traffic load,... Supply spectrum bandwidth, carrier frequency, spectral efficiency,... In this work, though, we will focus on traffic load. The other variables are modeled through a feasible average throughput and range for each BS type. No explicit assumptions will be made on what services that are demanded, except for that only moderate data rates (in the order of 100kbps) are required with full coverage. This is motivated by the rationale outlined in Chapter 1; that is, that only such services with low and moderate data rates should be economically viable to offer in mobile networks with wide-area coverage and that demand for these services therefore should drive network dimensioning. Quality of Service (QoS) characteristics, such as peak user data rate, outage probability, blocking probability, and delay requirements are consequently exogenous variables. Only aggregate traffic volumes (or average throughput) will be used as measure of demand. In practice, the users willingness to pay for a service is (simply put) a function of the value the service brings, competition, and availability of substitutes; see further Appendix A.2. This all varies both spatially and over time, and greatly affects a network deployment strategy. 3 However, to make accurate predictions of future demand for specific services and applications is very difficult. Moreover, already a few different service mixes would be quite tedious to model and analyze. Therefore, we will in this study resort to having the aforementioned macro-scopic parameters as a measure of demand. Moreover, only initial network deployment (roll-out) will be considered. 4 This is done to keep the analysis simple and tractable. However, since absolute cost estimates are not as important for us as relative comparisons between technologies, this should only have a minor impact on the conclusions. Emerging traffic demand and the evolution of component cost and performance will hence not be considered in this initial study. 3 For example, a residential user may have access to a flat rate fixed broadband service and is thus willing to pay less per bit for mobile data services than a vehicular user (ceteris paribus). 4 Normally, network capacity and coverage is increased gradually as demand increases.

37 2.4. Base Station Characteristics (Paper 1) 21 Strategic marketing issues are also neglected, as well as regulatory requirements, legacy infrastructure and organizational know-how and culture. These factors will, of course, all heavily affect the deployment strategy for an operator. However, they are outside the scope of this thesis which has a technical orientation. All in all, we do not consider the delimited factors as absolutely necessary for a first approximation of infrastructure costs. It is still, though, important to keep these delimitations in mind when making conclusions on viable deployment strategies based on the results. If more specific and precise results are required, detailed case studies needs to be performed. Only then can all the aspects listed above be modeled appropriately. 2.4 Relation between Base Station Characteristics and Cost Structure (Paper 1) In the first paper we present a model for estimating average infrastructure costs in mobile networks with uniform spatial traffic load. For this purpose, empirically based cost estimates of common BS configurations are derived [23]. The models and results are summarized next. Infrastructure Cost Model Motivated by the cost structure of mobile networks [1], only the BSs are included in our cost model. It should be noticed though, that all major auxiliary costs for a BS site can be included, and not only radio equipment. With different BS configurations available, the total infrastructure cost C for a mobile operator can thus simply be modeled as C = i B c i n i, (2.1) where c i is the cost for BS type i, n i is the number of BSs that would be required of that kind, and B is the set of available BS configurations. In this paper, however, we only consider a uniform traffic distribution and hence only one type of BS is considered (for each case). Network Dimensioning The network is dimensioned to serve a given average traffic density during the busy hour 5 and, for the sake of simplicity, only downlink is considered. This should be reasonable since downlink generally limits the aggregate capacity in a cellular system, while uplink limits the data rate per link and range (coverage) 5 A mobile network is normally dimensioned according to the traffic demanded during the busiest time of the day the busy hour.

38 22 Chapter 2. Infrastructure Cost Modeling when the traffic load is low [61]. The maximum average throughput (reflecting capacity) per BS is assumed to be constant, and does not vary as a function of the actual cell range or time-varying traffic load, which typically is the case in interference limited cellular networks. 6 With a uniform spatial traffic density the network is thus either limited by average aggregate throughput per BS W max ( capacity limited ) or the maximum cell range R max ( coverage limited ). The number of BSs required is then given by { Aservice N bs = max πrmax 2, N } userw user, (2.2) W max where A service is the total service area and W service is the total aggregate throughput to be served within that area. Furthermore, the cell radius can not be smaller than R min to assure that inter-cell interference levels are sufficiently low [61]. 7 Thus, all required QoS levels need to be feasible at the assumed cell range R (subject to R min < R < R max ) and average BS throughput W < W max. Base Station Performance Assumptions Performance and cost assumptions for a few typical cellular BS configurations are summarized in Table 2.1. All values are approximative, but should be representative in a relative sense for a typical WCDMA system deployed in a Western European city during Typical cell ranges and throughput estimates are based on [61] and do not represent the performance of any specific product. We have assumed that the cell capacity is higher for micro and pico BSs than in macro cells. This since it is possible to minimize inter-cell interference by a proper placement of the antennas (below roof-top or indoors). It should also be noted that, for the sake of simplicity, only single carrier WCDMA BSs were considered in this paper and that the macro BS is equipped with three sectors. The equipment costs estimates were provided by the Gartner Group and the other cost parameters are based on [1]. All cost data were slightly simplified according to our own assumptions to fit with the chosen system modeling. Results The total cost (in present value) for each BS type is presented in Figure 2.2, grouped by: Radio BS equipment and discounted O&M costs. 6 In fact, a cellular network is never hexagonally shaped with uniform interference statistics in practice. However, when modeling systems deployed over large areas it should be reasonable to assume that the average approach we have taken herein is sufficiently accurate. 7 Besides, macro BSs can not be too densily deployed due to practical and environmental reasons.

39 2.5. Heterogeneous Infrastructure Cost (Paper 2) 23 Table 2.1: Single carrier WCDMA BS performance estimates (based on own assumptions and [61]). Macro BS Micro BS Pico BS Sectors Maximum cell range (R max ) 1km 0.25km 0.1km Minimum cell range (R min ) 0.25km 0.1km 0.025km Sector throughput 0.75Mbps 1.25Mbps 1.75Mbps Maximum BS throughput (W max ) 2.25Mbps 1.25Mbps 1.75Mbps Sites installation and discounted site leases. Last mile -transmission discounted leased line costs. A more detailed description of the cost assumptions are given in the paper [23], and references therein. In this example micro BSs are 66% cheaper than macro BSs, whereas the cost for a pico BS is only 44% lower than a a micro BS. This has a rather intuitive explanation: the equipment cost is lower and site costs can be reduced significantly as the required cell range decreases. However, the transmission costs are the same for all BSs (which is a somewhat simplistic assumption). Hence, as observed in [41], for BSs with shorter range the cost is driven by transmission, rather than by radio and site costs. 8 The infrastructure cost per user is calculated as a function of user density, for different traffic levels per user during busy hour. With the modeling and assumptions outlined above, results show that single carrier (5MHz) WCDMA macro BSs are sufficient with less than, approximately, 4Mbps/km 2. This corresponds to voice users (at 20mE), or 400 data users downloading 5MB of data during busy hour, per km 2. At this traffic, approximately two macro BSs are thus needed per km 2, corresponding to a cell radius of 400m. In Figure 2.2 it is further shown for what user densities the respective BSs are range limited under these performance and traffic assumptions. However, since only a single macro-cell carrier was assumed in this example, these values can be be multiplied by three for the initial spectrum allocation typically available for European 3G operators. 2.5 An Infrastructure Cost Evaluation of Singleand Multi-Access Networks with Heterogeneous Traffic Density (Paper 2) While traffic was uniformly distributed in the first paper, a spatially heterogeneous traffic density is instead applied in this contribution [24]. The primary 8 The exact figures are of course case specific.

40 24 Chapter 2. Infrastructure Cost Modeling Base station cost structure Radio equipment and O&M Site installation and rental "Last mile"transmission 200 Cost [keuro] Macro Micro Pico Figure 2.2: Cost structure of typical urban WCDMA BSs calculated in present value over 10 years Average busy hour throughput per user W user = 1kbps Infrastructure cost per km 2 in present value [keuro] Pico BS Micro BS Macro BS Coverage limited Capacity limited Users/km 2 Figure 2.3: Total infrastructure cost per km 2 for different WCDMA BSs with uniform spatial traffic density.

41 2.5. Heterogeneous Infrastructure Cost (Paper 2) 25 purpose of this is to quantify the cost savings with hierarchical cell structures and multi-access networks under more scattered traffic variations, both with wide-area and hot spot coverage. Deployment strategies with spatially varying traffic density With a perfectly homogeneous, uniform, spatial traffic density a single type of BS will be sufficient to minimize cost. For example, micro cellular BSs may be cheapest with high area traffic demand whereas macro BSs typically yield the lowest cost where traffic is low. Hierarchical cell structures and multi-access networks are expected to be more cost efficient only in scenarios with heterogeneous spatial traffic densities. This is, however, not a sufficient condition; traffic peaks (hot spots) also need to be few and strong. Otherwise a large number of APs are required and there will most likely be excess capacity in the smaller cells. Heterogeneous Traffic Density Model User density is in this paper modeled as a log-normally distributed, spatially correlated, stochastic variable. 9 This is motivated by traffic distribution in GSM networks [63], and detailed population statistics available from the United States [64]. However, the model has not been verified with traffic measurements in mobile data networks. The traffic map is divided into subareas of 20x20m, where the standard deviation per sample has been chosen so that local peaks in user density are reached with reasonable probability. Notice, though, that an operator typically can not plan the network with that fine granularity in practice, at least not during rollout when demand is not well known. To fit the standard deviation per macro-cell (with a typical cell radius of 1km) to 0.4dB, reported in [63], a spatial correlation distance of 500m is assumed. By multiplying the user density with the average busy hour throughput per user, we get the average traffic to be served. As a reference case we will use private speech users, who typically call 1-2 minutes during the busy hour. This yields approximately 0.2kbps average throughput assuming a 10kbps voice service. Furthermore it is assumed that the studied operator has a 30% market share, and that overall service penetration is 90%. This gives, for example, an average user density of users/km 2 in an urban area with people/km 2, and users/km 2 in a city centre with people/km 2. Based on these numbers the average traffic per user can be approximated. 9 Similar to how shadow fading typically is modeled in simulations [62].

42 26 Chapter 2. Infrastructure Cost Modeling Macro Micro Traffic Density [log 10 (Mbps/km 2 )] Figure 2.4: Example of a network deployment with macro and micro cells covering an area of 4x4km with a spatially non-uniform traffic density. Summary of Network Dimensioning Algorithm A simple network dimensioning algorithm has been applied to account for the spatially varying traffic load. 10 In essence, macro BSs are deployed first to obtain rudimentary coverage. Micro BSs, Pico BSs, and WLAN APs are then deployed in hot spots in an increasing order of cell radius. Traffic in area samples with low demand (user density) is primarily allocated to the macro cells, and so forth. For the case of fractional (hot spot) coverage only, we include only the, for instance, 20-percentile of traffic with lowest cost per transmitted bit in the cost calculations. Notice that a more detailed description of the algorithm is available in the paper. The gray scale contour in Figure 2.4 depicts a realization of traffic density generated by the model, with a network deployment of macro and micro BSs. Notice that the micro BSs primarily are placed in areas with high traffic density and that all base stations are placed on regular, hexagonal, grids (with a specific cell radius for each type of BS). Base Station Characteristics The system concepts compared in the numerical examples of this paper include the single-access systems: 10 This algorithm was proposed by the other authors of the paper; Magnus Almgren and Anders Furuskär, both with Ericsson Research.

43 2.5. Heterogeneous Infrastructure Cost (Paper 2) 27 Table 2.2: Performance parameters and cost coefficients for the included radio access technologies (BSs, APs and relay clusters). Radio System Spectral Cell Capacity Cost Access bandwidth efficiency radius [Mbps] Technology [MHz] [bps/hz/cell] [m] 3G macro G micro G pico g HSDPA As 3G 0.5 As 3G 2.5x3G As 3G S3G macro x15 1 S3G micro S3G pico S3G x15 1 4G macro x G micro G pico G relay WCDMA with Dedicated Channels (WCDMA DCH), WCDMA with High Speed Downlink Packet Access (WCDMA HSDPA), IEEE g WLAN, preliminary Super 3G (S3G) and 4G proposals, as well as multi-access combinations of WCDMA DCH/HSDPA and IEEE g. As in the previous paper, QoS requirements (delay and peak data rates) are not modeled explicitly. For a fair and relevant comparison the services considered therefore need to be feasible to provide with the performance parameters given in Table 2.2. All performance estimates are based on an outdoor urban environment, whereby resulting costs may be slightly optimistic for indoor services. 11 Results In this paper the total system infrastructure cost, normalized per transmitted gigabyte (GB) of data per month, is compared for different combinations of radio access technologies. This has been evaluated as a function of average traffic density for both fractional (20%) and almost full (90%) coverage of the offered traffic; see Figure 2.5. As a reference level, the traffic volume relative to typical private speech users in a city centre (cc) and low-rise urban (u) environments are depicted on the horizontal axes. 11 Notice also that the WCDMA performance estimates are slightly different than in Paper 1.

44 28 Chapter 2. Infrastructure Cost Modeling The multi-access concepts IEEE g WLAN combined with WCDMA DCH respective WCDMA HSDPA macro and micro cells yield equal or lower cost than the single-access WCDMA solutions (with pico cells in hot spots). However, gains as compared to pure WCDMA DCH and HSDPA systems are evident only at very high traffic (100 and 300Mbps/km 2 respectively). 12 Among the studied future system concepts, S3G remains coverage limited for slightly higher traffic densities than WCDMA DCH and WCDMA HSDPA, and thus yields lower costs than these systems for traffic densities exceeding 2Mbps/km 2. Even lower cost can be achieved with the hypothetical S3G 450 system; due to its large cell radius, the cost is almost 10 times lower than for the other cellular concepts at low traffic densities. Even for high traffic densities S3G 450 is better than S3G, despite the same throughput per BS. This indicates that, even with a high mean traffic, there are large areas with less traffic where BS range still is important for cost efficient operations. For the case of 20% served traffic only, the main difference is that IEEE g yield a lower cost than WCDMA DCH and HSDPA already at 20 and 40 Mbps/km 2 respectively (instead of 100 and 300Mbps/km 2 ). 2.6 Cost Efficient Capacity Expansion Strategies Using Multi-Access Networks (Paper 3) In this paper [25], we test different combinations of WCDMA HSDPA macro BS and IEEE g AP densities to analyze the sensitivity of the infrastructure cost with respect to initial deployment strategy. The evaluation is done with the modeling and assumptions in the previous paper. Furthermore, we introduce the elasticity of infrastructure cost as a measure of how relative improvements of different sub-systems affect the total cost. That is, to identify what parameters that are most important to improve in order to reduce the cost of a multi-access network at different traffic loads. In the perspective of this analysis, we also discuss in the paper how cell range, throughput, and the cost per BS can be improved. Elasticity of Infrastructure Cost Elasticity is widely used in economics to measure the incremental percentage change in one variable with respect to an incremental percentage change in another variable [11]. In this study we evaluate the elasticity of infrastructure cost with respect to the cost c i, coverage area A i, and capacity W i per AP. We define the elasticity of a parameter X {c i, A i, W i } on the total infrastructure 12 Notice that the exact crossover points are subject to the performance and traffic modeling whereby these results are not generally applicable.

45 2.6. Cost Efficient Capacity Expansion (Paper 3) 29 Infrastructure Cost per Month per GB [Euro] u1 cc1 Fraction of Supported Users 20% u cc10 u100 Traffic Density [Mbps/km 2 ] (a) 20% served traffic cc g WCDMA DCH WCDMA HS DCH & 11g HS & 11g S3G S3G 450 4G 4G relay u1000 cc1000 Infrastructure Cost per Month and GB [Euro] u1 cc1 Fraction of Supported Users 90% u cc10 u100 Traffic Density [Mbps/km 2 ] (b) 90% served traffic cc g WCDMA DCH WCDMA HS DCH & 11g HS & 11g S3G S3G 450 4G 4G relay u1000 cc1000 Figure 2.5: Infrastructure cost per transmitted GB per month for a few different system configurations, including cellular systems, WLAN, and multi-access combinations. In the upper graph only 20% of the offered traffic is served, reflecting the coverage of a hot spot access provider, whereas in the lower plot 90% of the traffic is served.

46 30 Chapter 2. Infrastructure Cost Modeling Traffic density 1Mbps/km 2 10Mbps/km 2 50Mbps/km 2 HSDPA radius 1000m 800m 400m HSDPA BS density 0.33BSs/km BSs/km 2 2.2BSs/km 2 HSDPA cells/bs 1.4cells 5.7cells 6.8cells WLAN AP density 0APs/km 2 2.1APs/km 2 19APs/km 2 Infrastructure cost per GB per month: Incumbent operator e6.8 e2.9 e1.7 Greenfield operator e8.8 e3.4 e1.9 Cost advantage 24% 15% 10% for incumbent Table 2.3: Summary of monthly infrastructure costs per GB and the cost advantage for incumbents towards greenfield operators. cost C as: E C,X = C/C X /X. (2.3) Thus, a negative E C,X corresponds to a decreased cost and if E C,X > 0 the infrastructure cost increases independently of if the changed variable X is increased or decreased. The higher absolute elasticity, the greater impact X has on C. Notice that elasticity quite often is calculated in absolute value in economics [24]. As an example, assume that we want to estimate the elasticity with respect to the area covered per IEEE g AP. An elasticity of infrastructure cost E C,X = 1 would then correspond to that the total infrastructure cost C decreases with 50% if the cell area is increased with 50%. That is, if the AP range was = 49m instead of 40m. Results In this paper we first estimate the infrastructure cost per GB and month for a few different macro cell radii of an HSDPA system in combination with IEEE g APs. With denser macro cell layer, fewer WLAN APs will be deployed (in accordance with the network dimensioning algorithm described in Section 2.5). Due to the significant difference in cost and range of WCDMA HSDPA macro BSs and IEEE g APs, different cell radius in HSDPA will minimize cost at different traffic densities; see further Figure 2.6. With the assumptions and modeling applied, we see that the incremental cost per GB and month flattens at approximately 4Mbps/km 2. However, more macro cells are still beneficial as traffic increase. Otherwise too many APs will have excess capacity in areas with medium traffic. The results are summarized for a few traffic densities in Table 2.3.

47 2.6. Cost Efficient Capacity Expansion (Paper 3) 31 Infrastructure Cost per Month and GB [Euro] m 400m 800m 1000m Voice 10 x voice 100 x voice Average Traffic Density [Mbps/km 2 ] Figure 2.6: Infrastructure cost per GB and month for an incumbent operator with a multi-access network consisting of WCDMA HSDPA macro BSs and IEEE g APs. The curves depict different cell radii in the macro cells. The elasticity of infrastructure cost analysis shows that HSDPA capacity is slightly more important to improve than g coverage with a dense macro cell network (400m cell radius). However, with 800m cell radius improving HSDPA capacity yields twice as high cost reduction as g coverage which is shown in Figure 2.7. However, for traffic densities above 50Mbps/km 2 the result is the opposite. Furthermore, as seen in Figure 2.7, half of the total cost stem from each sub-system at approximately 10 and 100Mbps/km 2 with 800m and 400m cell radius in WCDMA HSDPA respectively. Thus, the benefits of improving different subsystems greatly depend on the initial dimensioning of the cellular system and the average traffic density. Positioning of Future Radio Access Technologies These results points at how a future 4G radio interface targeted for urban environments could be differentiated with respect to current main stream technologies for wireless data connectivity. Examples of base station configurations not covered well by today s systems (for urban deployment) are, as we see it, high capacity micro BSs and long range WLAN APs. Regarding the BS cost structure we can also note that, given the empirical data available and our assumptions, macro cells are dominated by site rent and installation. As observed in [18], this is probably difficult to reduce, both in ur-

48 32 Chapter 2. Infrastructure Cost Modeling Elasticity of Infrastructure Cost HSDPA cost HSDPA capacity g cost g coverage 1 Voice 10 x voice 100 x voice Average Traffic Density [Mbps/km 2 ] (a) 400m HSDPA cell radius Elasticity of Infrastructure Cost HSDPA cost HSDPA capacity g cost g coverage 1 Voice 10 x voice 100 x voice Average Traffic Density [Mbps/km 2 ] (b) 800m HSDPA cell radius Figure 2.7: The figure shows the elasticity of infrastructure cost for an HSDPA and IEEE g multi-access network with respect to improved AP costs, HSDPA capacity, and range respectively. In the upper figure, the cell radius of HSDPA is 400m (adapted for 100Mbps/km 2 ) and the lower graph depicts the results for a 800m cell radius (suitable for 10Mbps/km 2 ).

49 2.7. User Deployed Access Points (Paper 4) 33 ban environments with constantly increasing property prices, and in rural areas due to the construction work. WLAN APs are, instead, almost completely dominated by last-mil transmission. Considering indoor deployment, which is most common for WLAN, we could therefore foresee novel, distributed, deployment strategies involving the users and that is the focus of the next paper. 2.7 On the Cost Efficiency of User Deployed Access Points Integrated in Mobile Networks (Paper 4) An increasing availability of fixed broadband networks, including digital subscriber lines and cable modems, and the development of WLAN technology will enable new designs of public wireless access networks. In this paper [26], the economics of user deployed APs that are open for other subscribers and roaming partners is considered. More specifically, we calculate the infrastructure cost as a function of traffic density (area capacity) for different mixes of operator deployed BSs and user deployed APs. Furthermore, the number of APs required to serve different fractions of the offered traffic is estimated. Network Franchising Integrating user deployed APs in a public mobile network has, to the best of our knowledge, not yet been implemented in practice. Use cases and business models for that is an interesting topic, and we envisage that this is a plausible extension of the ongoing convergence between fixed and mobile systems. One possible business model that could be adopted by operators interested in exploiting this possibility would be network franchising ; meaning that users install APs, which the operator controls in terms of access rights, etc. A successful franchising agreement of course relies on that both parties benefit. In this case, the operator obtains accessibility to APs providing cheap, high-capacity, wireless access whereas the user gets an AP and some compensation by the operator. The operators could further on compensate the AP owner through bundling of different services, such as fixed broadband, subsidized access boxes, and wireless access when the user is in other locations. These marketing related issues are however outside the scope of this paper. Yet, we can note that network franchising in particular could be of interest to MNOs with limited spectrum and/or poor indoor coverage, or broadband providers that would like to exploit their fixed network by offering wireless access (indoors). Network Dimensioning with User Deployed Access Points In the operator deployed systems modeled in the two previous papers [24,25], BSs were deployed in a decreasing order of cell range on hexagonal grids. Herein, we

50 34 Chapter 2. Infrastructure Cost Modeling Percentage of traffic covered Typical fraction of traffic terminated indoors in today's mobile systems 30 20mE voice traffic x voice 200 x voice Percentage of subscribers with APs Figure 2.8: Fraction of traffic covered as a function of the percent of users with an open AP, for different data volumes. will also introduce user deployed APs in the analysis. In that case, user deployed APs are instead deployed first, in a random fashion with a uniform distribution. Residual traffic is then allocated to operator deployed BSs according to the previous algorithm. All cost and performance assumptions for WCDMA HSDPA and operator deployed WLAN (IEEE g) are the same as in [24,25]. User deployed APs, instead, resembles IEEE b with 50m range and 5Mbps average throughput. Moreover, the cost for a user deployed AP is 20 times less than for a operator deployed WLAN AP, including a revenue sharing of approximately e100 per year with each AP owner. Moreover, the cell radius of HSDPA is 1000m for traffic densities below 5Mbps/km 2, 800m between 5 and 20Mbps/km 2, and 400m for densities above that [25]. Interesting to note is that if 1, 2 or 4% of the subscribers install open APs, as much as 30, 40 and 70% of the traffic, respectively, is covered by these APs; see Figure 2.8. This indicates that user deployed APs could bring substantial cost savings with respect to traffic dimensioning. Results As in the two previous papers, we have herein evaluated the infrastructure cost per GB per month, but this time for a few levels of user deployed APs (measured as the percentage of subscribers with APs). As expected, the operator deployed

51 2.8. Conclusions 35 Monthly Infrastructure Cost [Euro/GB] HS+11g HS+11g+AP1% HS+11g+AP2% HS+11g+AP4% Traffic Density [Mbps/km 2 ] Figure 2.9: Monthly cost per transmitted GB as a function of average traffic density. The solid line is for an operator deployed multi-access network with HSDPA and g. The other lines represent different percentages of subscribers with open APs. network yields lowest cost at low traffic densities whereas user deployed APs is worthwhile introducing at approximately 10Mbps/km 2 ; see further Figure 2.9. At ten times that traffic density, the cost for operator deployed networks have flattened (as seen in the previous paper), whereas the incremental cost per GB and month still diminishes if instead more APs are deployed by the users. 2.8 Conclusions In this chapter we have compared a few alternative methods to reduce the cost of mobile data networks using various combinations of radio access technologies and BSs. In particular, we have compared multi-access networks consisting of WCDMA and WLAN, with conventional single-access hierarchical cell structures. Future network concepts (S3G, 4G, etc.) have also been considered, as well as user deployed APs. For this purpose, we proposed a model to estimate the cost of a radio access network as a function of traffic density. The model is based on average cost and performance data, and accounts for both investments and running costs (per BS). To dimension the network we utilize a statistical model for geographically varying traffic demand. No explicit QoS-requirements or service mixes are assumed, instead, the maximum feasible cell range and average throughput

52 36 Chapter 2. Infrastructure Cost Modeling ultimately determines what services that the network is capable to deliver. The key numerical results are summarized next, followed by a discussion of the need for multi-access networks and factors that affect the validity of the presented findings. Infrastructure Cost Structure We have seen that the cost structure of a wireless system largely is a function of the characteristics of the deployed BSs. For a macro BS the costs mainly considers BS equipment, O&M, installation and site rent, whereas last mile - transmission dominates for pico BSs and operator deployed WLAN. Multi-access networks, composed of WCDMA macro and micro cells combined with IEEE g WLAN APs, do not bring lower costs per user than single-access networks. On the other hand, there is no loss either. From an infrastructure cost perspective, there should hence be no major disadvantage for an MNO to introduce WLAN instead of pico BSs in hot spots. Amongst the hypothetical future network concepts studied, the overall lowest cost is enabled by a S3G system operating in the 450MHz band. The long range, in combination with a high throughput, is thus beneficial also at high average traffic loads. Provided that wide-area coverage is demanded for the considered services, we can thus conclude that range also is important to improve (or at least maintain) if traffic demand increases, and not only aggregate throughput per BS. We have also illustrated how the elasticity of infrastructure cost can be used to analyze what design parameters, including both performance and costs of different systems, that are most important to improve in a multi-access network. In an example, we probed a bit deeper into the case of a combined WCDMA HSDPA and IEEE g network. For this specific case, the aggregate throughput per HSDPA macro BS was shown to be more important to improve than the range of IEEE g in the case of a dense macro cell network (with 400m cell radius). If, instead, HSDPA BSs are more sparsely deployed (800m cell radius), similar cost savings can be achieved by increasing the range of IEEE g already at moderate traffic loads. Thus, what parameter to improve in different subsystems can not be generalized; it depends on the original network design and the level of traffic demand. Despite that hierarchical cell structures and multi-access networks reduce cost, however, the incremental cost per GB always flatten at some traffic load for operator deployed systems (for a given level of technology). Incorporating user deployed APs in the network would then be an attractive method to increase capacity even further at a (still) diminishing incremental cost. With a few numerical examples, for different fractions of users equipped with open APs, we showed that the overall infrastructure cost can be reduced significantly. This even with a considerable amount of revenue sharing with the AP owner.

53 2.8. Conclusions 37 The Need for Multi-Access Networks Due to the minor difference in cost we have observed for a multi-access network as compared to single-access systems, it is plausible that other factors than aggregate traffic volumes in the end will have greater impact on an MNOs technology strategy. Thus, as described in Section 2.3, a thorough investment analysis is required to make recommendations on an infrastructure deployment strategy. A few general reflections could still be made, to put the presented results in a broader perspective. While advantages with a multi-access deployment strategy include increased peak user data rates, less need for licensed spectrum and reduced investments, a drawback is that it typically is more complicated to manage multiple systems (both in terminals, and in the central systems). Furthermore, already the initially deployed WCDMA networks have a cell radius of a few hundred meters in urban areas. This was necessary in order to obtain sufficient indoor coverage for services like video streaming and lap-top connectivity with data rates in the order of kbps. Thus, UMTS networks are in practice quite often coverage limited up to quite high traffic densities also in urban areas. The need for additional WLAN and cellular micro and pico BSs in such scenarios should hence primarily be to improve coverage for higher data rates in specific places, for example in malls and large office buildings, and not for capacity reasons. For greenfield deployments, however, a multi-access network could be considered from the beginning and thereby enable a sparser macro-cell network. Of particular interest should then be the opportunity to introduce user deployed APs in the network. Still, WLAN in general, and user deployed APs in particular, may not be sufficient for services that require good coverage, reliability, and QoS. In that case, a mobile network is still useful for rudimentary coverage and capacity. Validity of Results To conclude this chapter, we will discuss a few important assumptions and choices of modeling which could affect the accuracy of the presented results. Firstly, all numerical results are subject to our specific assumptions. Although the intention has been to use fair and realistic parameter settings, these may of course differ significantly for a real deployment case. The spatial traffic model was derived from population statistics and traffic measurements from GSM networks. It is not certain that mobile data users will have a similar behavior, even though that may be a reasonable assumption for the (still) moderate data rates being the focus of this work. Therefore, empirical data on traffic demand for mobile data services would (when such become available) be useful to improve the heterogeneous traffic density model. Furthermore, we did not model QoS requirements explicitly, and the resulting data rates, etc., could therefore vary significantly between compared system configurations. Besides, no attempts have been made to optimize the network

54 38 Chapter 2. Infrastructure Cost Modeling deployment. Instead, the target has been a simple principle that is reasonably good and fair between system concepts. Still, although we elaborated on different cell radii in Paper 3 [25], a more extensive sensitivity analysis is of interest for further studies. Moreover, the network dimensioning model is primarily not intended for high rise buildings, where a 3-dimensional model is needed. This naturally limits the scope of the study at hand. It should also be noted that the user deployed APs were uniformly distributed. This may be a pessimistic assumption for estimating population (or household) coverage, since more APs automatically will be placed where many people live. However, high population peaks are most often where there are high-rise buildings so with our 2-dimensional model, the result would be too optimistic if APs are deployed proportional to traffic demand. Regarding cost estimates, we have relied on secondary data in almost all cases. However, also here, the ambition has been to model the relative difference between technologies, in an average sense, as fair as possible. It should be stressed though, that a linear annualization of CAPEX would be even more straightforward than the present value calculations we have utilized, and probably yield similar results. 13 Yet, in spite of the simplistic assumptions used herein, the proposed methodology should, much thanks to its simplicity, be useful for an initial assessment of operator deployment strategies and when technical requirements are defined for future radio access technologies (for instance in standardization bodies). 13 In fact, we would recommend a linear annualization as a base line assumption for further studies in this area.

55 Chapter 3 Multi-Operator Resource Sharing Herein, we will consider three different cases of multi-operator resource sharing, which differ greatly from a business perspective but have quite a lot in common technically. Firstly, network sharing has evolved as an important method to reduce investments in wireless infrastructure, especially in rural areas for systems and services with limited link budgets. This can, as discussed further in Appendix B and in the following literature study, either be implemented as a common shared network or by means of geographical sharing. Secondly, the market for MVNOs who, with little or no mobile infrastructure, offer wireless services is steadily increasing. Thirdly, MNOs could potentially agree to allow for roaming in between their existing networks in order to increase coverage and capacity for higher data rates (hereinafter denoted national roaming). The common technical denominator for these flavors of network sharing is that they can be facilitated with functionality originally designed for international roaming. Therefore, we will refer to them jointly as roaming based network sharing. A potential drawback with roaming based network sharing is that forecasting of traffic demand could become less transparent to the network provider. Hence, given that over-dimensioning remains expensive, the post-paid charging of MVNOs (see further Appendix B.2) may be insufficient for network planning purposes. Furthermore, problems could arise in terms of free-rider effects and there is a risk that competing service providers provide consciously misleading traffic forecasts. Thus, besides assuring certain QoS levels for the roaming partners, those should also be punished in some way if the actual traffic exceed the contracted volumes significantly and this topic will be addressed in this chapter of the thesis. Moreover, we will investigate what performance benefits that can be expected for data services by sharing overlaid cellular networks via national roaming. Al- 39

56 40 Chapter 3. Multi-Operator Resource Sharing though that was technically feasible already in earlier generations of mobile systems it has, to the best of our knowledge, not been implemented in practice so far. Partly because of the limited bandwidth required for voice telephony, but also due to competitive reasons; coverage has simply been seen as a major competitive advantage [28]. However, if the focus of operators and competition authorities shift towards applications and services, and due to the inherent difficulties to provide wide area coverage for higher data rates, national roaming may be worthwhile to reconsider for future radio access. In particular when introducing new bandwidth demanding services, for which demand is uncertain, that require significant area coverage. National roaming could in that case even be of interest for the MNOs in order to reduce upfront investments (and, thus, their risk exposure) Related Work Previous studies on network sharing mainly considers techno-economical aspects, including cost savings [1, 27, 29], competition [30, 65], and identification of new requirements on network management [66, 67]. While fair resource sharing has been addressed in numerous papers, both for fixed and wireless networks, previous work have mainly considered individual connections and service classes sharing a common link. Besides this, some initial work on fair resource sharing in multi-operator networks have recently been presented [68]. Moreover, spectrum sharing has recently appeared as part of the ongoing research targeting 4G [69, 70]. Potential Cost Savings The financial benefits of network sharing were analyzed in [1, 27 29]. These studies show that cost savings are substantial, in particular in rural areas where capacity utilization is low. In areas with higher user density the cost per subscriber is sufficiently low with single-operator networks, and the drawbacks with network sharing (reduced differentiation possibilities, administrative overhead, etc. [27]) are thus not justified solely by cost savings. In the short run, though, network sharing could still be used to shorten the time to market for new services and radio access technologies using, for example, geographical sharing. If demand surges the operators can expand their networks to provide full coverage. 1 Notice that for niche service providers requiring high data rates and reliability, for example for video surveillance cameras, this could of course also readily be implemented by using multiple subscriptions.

57 3.1. Related Work 41 Competition Competition amongst sharing UMTS operators was studied in [30, 65], focusing on the Swedish market. A key competitive advantage for MNOs has been to provide good area coverage. With network sharing, other differentiation opportunities are hence needed. Quite a few possibilities were identified in [30, 65], of which investing in multi-access networks was the most important 2, together with services that can be implemented in the unshared domain (that is, in the core network and above). Still, most services require modifications also in the shared RAN whereby site sharing is the only level of network sharing which does not severely limit the operators differentiation possibilities [30]. Some drawbacks and difficulties with geographical sharing were also outlined in [27]. This study emphasized that Marketing campaigns launched by competitors may boost traffic load beyond the current network capacity, resulting in severe blocking for your own customers. Customer driven coverage 3, which is quite common at enterprises, may only be provided by the operator responsible for that area. Network quality needs to be sufficient for your customers and the services you promote. All in all, both common shared networks and geographical sharing will, without further considerations, cause both administrative and competition related problems [27 30, 65]. These, and more practical technical problems, have also been addressed in the standardization body behind 3G 4, and the standards for UMTS have been updated accordingly to support the most fundamental features of shared networks [66, 71]. This includes operator specific neighbor cell and access rights lists, display of the home operator name in the terminals, etc. However, as pointed out in [30], there are many aspects hidden in the detailed configuration of a cellular network which also limits differentiation possibilities. Service Level Agreements A so-called Service Level Agreement (SLA) between the service provider and hosting RAN provider should include the requirements and responsibilities for both parties, considering for example QoS levels, reliability, performance monitoring, customer support, pricing policies, etc. [72,73]. An exhaustive recommendation for telecommunication SLAs has been provided by the Telecommunication Management Forum [74]. 2 In order to limit the scope of the shared UMTS network, the coverage of alternative technologies such as WLAN and EDGE could be expanded. 3 Operators sometimes deploy special indoor solutions to improve coverage and capacity for their corporate subscribers. 4 Third Generation Partnership Program (3GPP)

58 42 Chapter 3. Multi-Operator Resource Sharing While the short-term fulfillment of the terms in an SLA is handled by RRM, the service level monitoring and long-term actions to assure the contracted QoS levels are primarily a task of network management [73]. The latter also includes the procedure of mapping traffic forecasting onto needs for network capacity. There are, however, no well established methods or processes for this so far [73]. In order to share the risk with large MVNOs, the access provider could potentially base inter-connection charging on the share of the network capacity that the MVNO is granted (similar to a network sharing agreement) [29]. Pricing models for this scenario was also discussed in [72], where the author outlined a few parameters that should be included in an SLA. Fair Resource Sharing Between Operators The resource allocation problem for shared cellular networks was recently treated in [68]. Two principle methods to allocate radio resources fairly between sharing operators was considered for a WCDMA downlink system. One with fixed power allocations, which reduce trunking efficiency, and one based on adjusting elastic data rates for packet switched bearers. None was found to improve fairness significantly as compared to a reference system without any specific sharing mechanism, although lowering data rates for the operator with highest load at congestion gave a slight improvement. Spectrum sharing between cellular operators was investigated in [69, 70]. For the case of two cellular operators sharing the same frequency band it was concluded that trunking gain leads to increased capacity. However, the gain vanished with displaced BSs due to near-far effects. Notice that, in this thesis, we will only consider the case where multiple operators fully share the same network. The general problem of allocating radio resources to different bearer classes was treated in [19]. Fair sharing between individual connections sharing the same wireless system has also been treated in numerous papers considering packet scheduling between connected bearers. The objective of these studies is typically to maximize system capacity, often measured as the number of supported users [19], while assuring the QoS for individual connections. This problem is a tradeoff between the bearers resource consumption, delay and throughput requirements. To not complicate that further, by adding a constraint on how many radio resources that should be allocated to each operator, we will instead try to solve the problem of fair resource sharing between operators already in admission control. 3.2 Contribution Radio resource sharing between operators is an interesting option to reduce infrastructure costs. With roaming based network sharing a more formal structure with negotiated capacity requirements between the service and RAN providers

59 3.3. Fair Resource Sharing (Paper 5) 43 could be beneficial. In Paper 5 and 6, we have contributed to a framework for radio resource sharing in multi-operator networks and analyzed the technical performance benefits with national roaming. The papers are reprinted in Chapter 9 10, and the main contributions of the respective paper are as follows: 5. A basic algorithm for fair resource sharing was proposed and evaluated with a simple queuing system model. The algorithm is based on nonpreemptive priority queuing in the admission control, and thus no capacity has to be reserved to achieve fairness (at the expense of increased call setup times). 6. The achievable user data rates with national roaming between equivalent overlaid cellular networks are estimated using a statistical model for uplink best effort data users. In particular, the effect of operator specific BS sites is studied, and for this purpose a model for inter-operator site distance is introduced. 3.3 Radio Resource Management in Roaming Based Multi-Operator WCDMA networks (Paper 5) In this paper [32], we introduce the problem of fair resource sharing in multioperator cellular networks, and propose a load based priority queuing in the admission control to keep down the probability of call blocking for operators that have not reached their agreed load. The focus is on roaming based sharing, which means that an operator access another operators RAN indirectly via the core networks. This implies that multiple operators fully share the same RAN, which motivates a radio resource control between the operators. Normally the operators share the same carrier(s), but it is also possible to use dedicated carriers. Besides roaming based sharing, there are today two other major categories of network sharing, being RAN sharing and site sharing. The three groups of solutions imply different levels of sharing, which is depicted in Figure 3.1 (see further Appendix B). Methods for Allocating Radio Resources How much of the radio network capacity that each sharing partner has the right to use with roaming based sharing is commonly specified in an SLA. An operator that follows its terms in the SLA should receive the agreed QoS levels; this even if the other operators try to utilize more than agreed capacity. This implies that radio resources must be shared in a controlled way between the operators and this can in principle be achieved by:

60 44 Chapter 3. Multi-Operator Resource Sharing Core network Roaming based sharing Radio access network RAN sharing Site sharing BS BS BS BS Figure 3.1: The figure illustrates what levels of a cellular network architecture that different sharing methods relate to. using dedicated carriers for each operator, allocating a fixed capacity share for each operator per carrier, or dynamically prioritizing users from different operators (within one or multiple carriers). Dedicated carriers and fixed capacity shares provide fairness between operators. Unfortunately, though, any capacity reservation scheme implies a reduced capacity utilization (trunking efficiency). Instead, a dynamical prioritization of operators based on the current load is preferable. For this purpose standard RRM functionality such as admission control and packet scheduling can be utilized. In this paper we limited the scope to admission control, which in this context is responsible for admission of new connections (both packet and circuit switched) [61]. 5 Furthermore, assuming that a minimum rate is required for all admitted bearers, we will without loss of generality limit the study to circuit switched traffic. Queuing System model For the analysis in this paper a standard queuing system model with Poisson arrivals will be used [75]. The total offered load per operator i is denoted O i, and is defined as O i = λ i T where λ i is the average arrival rate of new connections for operator i and T is the average duration per connection. The total offered load is then given by N O = O i, (3.1) i=1 5 Packet scheduling, on the other hand, is responsible for adjusting the bit rate and thus resource consumption of connected non real-time radio bearers. Fair sharing using elastic bit rates was studied in [68].

61 3.3. Fair Resource Sharing (Paper 5) 45 assuming that N operators share the network. Each of these operators has priority to C i channels per cell. A new connection request is queued until there is a channel available. However, if the waiting time T d exceeds a certain threshold T max the request is blocked. The total number of channels per cell C = C i is thus modeled as constant, which is a simplistic assumption for an interference limited system. However, for an initial assessment of a priority queuing method, we believe that the allowed queuing time, average connection duration, and the total number of channels per cell will have stronger impact on the performance. More detailed system modeling is left for further studies. Admission control with Non-Preemptive Priority Queuing As a basic approach to solve the fair sharing problem we propose to use admission control with non-preemptive priority queuing. Connection requests of different operators are prioritized according to the respective operators load level relative to their agreed capacity. Preemption, that is, allowing for removal of existing connections to make space for a new request with higher priority will not be exploited. Although preemption would increase fairness, it would increase the probability of dropping active connections. 6 An overview of the applied admission control algorithm is depicted in Figure 3.2. The priority level of each operator (P i ) is defined as P i = C i L i, (3.2) so that operators with a load L i lower than the agreed minimum capacity C i receives a high priority. L i is simply defined as the total number of allocated channels for that operator at a given point in time (without any averaging). The queuing management outlined here can for example be implemented in conjunction with a periodical admission control and it consists of the following steps: 1. A new connection request that arrives when the system is congested (that is, when L i = C) is put in the queue. 2. The queue is periodically sorted in a descending order according to P i. Then each operator s connections are sorted group-wise in a descending order based on T d. Consequently, the operator with highest P i will be served first and each operator s connection requests are served in a firstin-first-out (FIFO) manner relative to each other. 3. If a channel has been released, the first user in the queue is admitted. 6 In general this is considered to be the most important QoS measure for circuit-switched services (like voice telephony).

62 46 Chapter 3. Multi-Operator Resource Sharing Put new connection request(s) in queue Sort queue: 1) P i 2) T d No Channel available? Yes Allocate channel No T d >T max? Yes Connection blocked! Figure 3.2: Flowchart of periodical admission control with non-preemptive priority queuing. 4. Connection requests for which T d > T max are blocked and removed from the queue. Results As a measure of the performance of the algorithm, we consider to what extent operator specific blocking probability B i can be kept below some given threshold B max (say 5%) if the load O i < C i. Hence, as long as an operator has not exceeded its agreed load share, their users should experience the contracted blocking probability. In an example with two operators sharing a network 50/50, we show that the proposed algorithm functions well for a system with C = 80 channels per cell. This case should resemble an urban WCDMA macro cell with voice users only. A queuing time T max = 5s is allowed for new connection requests. With only 16 channels per cell, modeling a video streaming service in WCDMA, the algorithm is less effective even though T max was increased to 15s. The operator specific blocking probabilities are plotted as a function of traffic load for both systems in Figure An Estimation of the Achievable User Throughput with National Roaming (Paper 6) As discussed previously, despite the introduction of advanced transmission and packet scheduling techniques, existing mobile data networks would require significant investments in order to support higher data rates with wide area cov-

63 3.4. Throughput with National Roaming (Paper 6) 47 Blocking probability for operator 1 (B 1 ) Fixed reservation O 2 = 80 Erl O 2 = 60 Erl O 2 = 40 Erl O 2 = 20 Erl Offered load for operator 1 (O 1 ) [Erlang] (a) Speech system with 80 channels per cell Blocking probability for operator 1 (B 1 ) Fixed reservation O 2 = 14 Erl O 2 = 10 Erl O 2 = 6 Erl O 2 = 2 Erl Offered load for operator 1 (O 1 ) [Erlang] (b) Video streaming system with 16 channels per cell Figure 3.3: The figure depict the blocking probability of operator 1 (B 1 ) as a function of offered load (O 1 ) for different levels of load for the other operator (O 2 ). In the upper graph, a speech service is assumed whereas the lower graph show the results for video streaming. The dashed lines represent a fixed resource allocation of half (C/2) channels per operator.

64 48 Chapter 3. Multi-Operator Resource Sharing erage. Consequently, many operators have chosen to postpone the necessary network upgrades until consumer demand becomes more pronounced. Although this might seem sound at a first glance, this is definitely not the case since supporting high-end users ( early adopters ) has proven to be a crucial enabler for reaching a mass-market later on [76, 77]. An alternative method to increase coverage for higher data rates without additional investments in hardware would instead be to allow users to roam between existing networks with, at least partially, overlapping coverage; herein referred to as national roaming. 7 In this paper [33], we investigate what throughput gains users can expect with roaming between multiple overlaid cellular networks. Moreover, we studied to what extent these gains depend on the microscopic diversity order and the relative location of the operators BSs. The study is limited to uplink transmission, since that typically limits coverage in cellular systems. Base Station Deployment Model A multi-operator environment with J = 3 operators is investigated and users are either allowed to connect to their own operators network only, or to any of the J cooperating operators BSs. For the sake of simplicity all operators are assumed to deploy similar BSs on a hexagonal grid with cell radius r c. Each operator has one carrier frequency, which is used in every cell (reuse factor 1). The relative position of the sites belonging to different operators is captured by the inter-operator site distance (d), which we define as the minimum distance between adjacent sites belonging to different operators. Thus for a scenario with J operators, ( ) d = min d 1, d 2,..., d ( J. (3.3) 2) Herein, we will consider the special case when d = d 1 =... = d ( J. For this case, 2) with three-operators, the relationship of the operators site locations is given by ( ) ( ) ( ) xi x0 cos(π/6) = + d ( 1) i, (3.4) sin(π/6) y i y 0 where i = 1, 2 and (x 0, y 0 ) T is the position of the reference network. Figure 3.4 depicts an example where d = r c. Summary of Cellular System Model More detailed system modeling and parameters are given in the paper, but for convenience the utilized models are summarized next and the main system 7 Notice that geographical sharing also has been called national roaming in the literature. Although the cases are different, a common denominator is that operators allow users from other operators to roam in their networks and that they retain the possibilities to expand their own network capacity if demand surges.

65 3.4. Throughput with National Roaming (Paper 6) 49 d d 1 7d2 500 r c Figure 3.4: An example of the BS deployment for a three-operator scenario and inter-operator site distance d = r c =360m. It should be noted that the maximum value of d is periodic with a period 2 3r c parameters are provided in Table 3.1. To start with, all users are stationary and uniformly distributed over the service area. This results in a Poisson distributed traffic per operator and cell. Moreover, for simplicity reasons, we assume that all users have full buffers and thus have data to transmit all the time. Propagation channel modeling is chosen to mimic an urban macro-cell environment, with distance dependent path loss according to the COST231-Hata model [78]. Shadow fading is log-normally distributed with 8dB standard deviation and 20m correlation distance [62, 79]. Rayleigh fading is also included, and the fading component is independent for each receive antenna (we will use two and four antennas). Moreover, for the sake of simplicity, there is no multi-path fading. The average transmit power was multiplied with the number of users connected per cell to compensate for reduced transmission time. However, the peak transmit power was limited to eight times the average in order to have a limited dynamic range. Two different packet schedulers have been studied, in order to vary the degree of diversity gain against Rayleigh fading. These were: Round Robin scheduling, with time-hopping to randomize interference, and Proportional Fair scheduling of the user with instantaneously most favorable fast fading.

66 50 Chapter 3. Multi-Operator Resource Sharing Table 3.1: System Parameters Parameter Value Population density [km 2 ] 5000 # Operators 3 Slot duration [ms] 2 Correlation distance [m] 20 Standard deviation (shadow fading) [db] 8 Shadow fading correlation between BSs 0.5 Pathloss@1m [db] 35.8 Distance dependent attenuation factor 3.5 Cell radius [m] 360 Channel bandwidth [MHz] 5 Thermal noise floor [dbm] -103 # Receiving antennas 2, 4 Average terminal power [dbm] 24 Maximum terminal power [dbm] 33 # Sectors per BS 3 Traffic [users/cell] 1, 3, 10 Inter-operator site distance [m] User throughput is estimated using a truncated Shannon model, where the maximum data rate was selected so that approximately 10% of the users reach this rate in the single operator case (with the modeling and assumptions used; 20Mbps). 8 The resulting SIR with maximum ratio combining is calculated as the sum of SIR (including thermal noise) over each antenna branch [80]. While Round Robin scheduling is used as a reference case, the Proportional Fair scheduler will in conjunction with four receive antennas model a system with a high degree of diversity against Rayleigh fading. Results The gain with national roaming was in this paper presented with respect to the lower 10th percentile and average user throughput; see further Figure 3.5. The difference in throughput is largest for the 10th percentile, for which national roaming increase user throughput as compared to the single operator reference system with approximately % with two receive antennas and Round Robin packet scheduling. It is interesting that an 80% gain is obtained already when BSs are just slightly separated. This can be explained by diversity gain against 8 This modeling is motivated by i) that modern radio access systems operate very close to the well known Shannon bound at low mobile velocity, and ii) that a system typically is designed so that the maximum rate is reached with some reasonably low probability.

67 3.5. Conclusions 51 shadow fading. 9 Average user throughput is increased with approximately 20-50%, and a similar dependency on inter-operator site distance can be observed as for the 10-percentile. With four receive antennas and proportional fair scheduling, which gives a higher order of diversity gain against Rayleigh fading, the relative increase in throughput is smaller (although still significant). The gain in user throughput is in this case between % for the 10-percentile whereas the average user throughput is increased with approximately 15-30%. The reduced gain with a higher order of diversity gain can be explained by the logarithmic rate function (with a higher SIR, the systems could have exploited a wider bandwidth). 3.5 Conclusions Motivated by arising business needs, we have in this chapter first outlined how fair resource sharing can be assured for operators using the same RAN. For this purpose, an admission control with non-preemptive priority queuing based on operator specific load was proposed. With a few numerical examples, we showed that the method is promising for systems with a large number of channels per cell. In this case, a quite moderate queuing time is sufficient. For systems with fewer users per cell, stricter methods exploiting resource reservations or preemption of allocated bearers would be necessary for a fair resource sharing. However, this would come at the expense of decreased capacity utilization or increased probability of dropped calls. The benefits with national roaming between overlapping macro cellular networks were also assessed for uplink data services. It was shown that user throughput can be increased significantly already with adjacent BS locations, and in particular for users with high path loss. Thus, national roaming promises large coverage gains with only minor incremental infrastructure costs. However, the relative gains will be smaller if the system already operate at high SIR levels. Discussion and Validity of Results Simplistic statistical modeling have been used for the numerical evaluation in both Paper 5 (Fair Resource Sharing) and Paper 6 (Throughput with National Roaming). Thus, the numerical results presented herein are indicative only and the performance in real systems may differ significantly from these. A more detailed system modeling is consequently needed before more specific conclusions can be made. In particular, a radio resource based admission control should be included (for both problems), and methods for estimating the operator specific load are required. In addition, it would be interesting to combine our algorithm with the 9 Herein, an urban scenario with 20m correlation distance was assumed for the log-normal shadow fading. In, for example, suburban environments a larger separation of base stations would be required to obtain such gains.

68 52 Chapter 3. Multi-Operator Resource Sharing Gain in 10th percentile user throughput with national roaming [%] Two receiving antennas and round robin scheduling 1 user/cell 3 users/cell 10 users/cell Inter operator site distance 78m Inter operator site distance 78m Inter operator site distance 624m Four receiving antennas and proportional fair scheduling 1 user/cell 3 users/cell 10 users/cell Inter operator site distance 624m (a) Lower 10-percentile user throughput Gain in average user throughput with national roaming [%] user/cell 3 users/cell 10 users/cell Two receiving antennas and round robin scheduling Inter operator site distance 78m Inter operator site distance 78m Inter operator site distance 624m Four receiving antennas and proportional fair scheduling 1 user/cell 3 users/cell 10 users/cell (b) Average user throughput Inter operator site distance 624m Figure 3.5: The relative gain with national roaming in 10-percentile user throughput (upper graph) and average user throughput (lower graph). For both measures, two different system configurations with different order of diversity against Rayleigh fading have been simulated.

69 3.5. Conclusions 53 elastic bit rate method proposed in [68] for the case of mixed circuit and packet switched radio bearers. A theoretical analysis of the priority queuing model presented in Paper 5 would also be useful in order to validate the simulation results and explain the behavior of the algorithm more consistently. National roaming has herein been discussed in the context of cellular operators. A major obstacle for that to materialize, however, is that coverage still is a very important competitive advantage for some operators. Yet, it is interesting to understand what the gains would be, and it should be noticed that national roaming also could be of interest for the emerging public WLAN operators and other wireless operators with fractional coverage only.

70

71 Chapter 4 Concluding Remarks This chapter concludes the first part of the thesis with a summary of the presented results and a few recommendations for future research in this field. 4.1 Summary The main theme of this thesis has been cost efficient provisioning of mobile data services. We have focused on two methods of relevance for MNOs; adopting network deployment to local variations in traffic demand and network sharing between multiple operators. Both solutions are, as we see it, quite promising and the key findings are summarized next. Infrastructure Cost Modeling We first assessed the costs of multi-access networks and compared these to conventional hierarchical cell structures (with a single radio access technology). For this purpose a network cost model, which accounts for the average cost and performance of different BSs, was developed. The overall infrastructure cost is calculated for a network composed of one or several radio access technologies. To model local variations in traffic, which are necessary to study the need for specific hot spot -solutions, we generated statistical traffic maps according to a log-normally distributed, spatially correlated, stochastic variable. With a few examples, covering both existing and proposed future systems, we also illustrated how the model can be used to estimate at what average traffic densities different systems are most cost efficient. With the modeling and assumptions used, multi-access networks consisting of WCDMA macro and micro-cells in combination with IEEE g WLAN always yield similar or lower infrastructure costs as compared to single-access WCDMA networks with hierarchical cell structures. Hence, from this perspective, WLAN should serve as a viable substitute for pico-cells in hot spots. How- 55

72 56 Chapter 4. Concluding Remarks ever, even though both multi-access networks and hierarchical cell structures manage to lower the cost to some extent, the cost will always increase linearly with traffic demand at some level (with a given set of technologies). Using the elasticity of infrastructure cost as performance measure, we also illustrated at what average traffic densities the throughput, range, and cost of different radio access technologies should be improved (when deployed in a multi-access network). This was exemplified with a network of WCDMA HSDPA macro cells and IEEE g APs. For this case, we observed that throughput is most important to increase for macro-cell BSs (at a maintained range), whereas increasing range and reducing cost for sites and last mile -transmission have a higher impact for WLAN. As a comparison, we also evaluated both future cellular concepts (S3G, 4G, etc.), as well as open, user deployed, WLAN APs combined with a WCDMA HS- DPA macro-cell network. These results point at two key directions for expanding network capacity if demand for mobile data services increase significantly. That is, an operator could either choose to increase capacity in their macro-cells at a (at least) maintained cell range, or resort to user deployed WLAN APs exploiting existing fixed broadband networks for the last mile -transmission. Multi-Operator Resource Sharing Two issues related to network sharing between multiple operators have also been addressed. For this scenario, we studied how radio resources can be allocated in a fair manner between operators sharing the same RAN. More specifically, an admission control of new connection requests (calls) with a non-preemptive priority queuing based on operator specific load was proposed. As an initial step, using queuing system simulations, we showed that the method is promising for systems with a large number of channels per cell. Hence, for cellular systems, this method should mainly be of interest for macro cell BSs serving many users simultaneously. With a few users per cell only, other methods relying on capacity reservations or preemption of connected bearers have to be considered. Finally, we estimated what gains in user throughput that can be expected by allowing for roaming between overlapping cellular networks. This type of national roaming could, for instance, be utilized by MNOs in order to try out new services without taking on large upfront investments. With a statistical system model for uplink best effort data traffic, we showed that gains are significant for the users that experience lowest throughput. Interesting to note is also that, thanks to increased diversity against shadow fading, a significant portion of the gain is present already with almost co-located BSs. 4.2 Future Work Departing from these results, a number of interesting research problems arise. With the proposed network deployment model, a natural extension would be

73 4.2. Future Work 57 to estimate the empirical distribution of data rates that users experience with different technology mixes. Refined system modeling and various sensitivity analyses are also of great interest and importance for the cost evaluation. In particular, the spatial traffic modeling should be verified for mobile data services, and the effect of different user distributions should be investigated. If possible, local demand and pricing for higher user data rates should also be modeled. From a techno-economical perspective, case studies with empirical data from specific markets and scenarios are also relevant extensions of this work. In a broader sense, future research considering multi-access networks should also include marketing strategies, multi-mode terminal availability, legacy infrastructure, cross-elasticity of demand between systems, etc. As this is a fundamental part of future wireless networks, it should be an important topic on the wireless research agenda and in particular for techno-economical research. For what concerns fair radio resource sharing between operators, more detailed system modeling is needed with respect to the characteristics of a cellular network (with time-varying path gains, interference, etc.). Furthermore, the proposed priority queuing mechanism relies on operator specific load measurements. This could be difficult to measure in interference limited systems, and averaging over both time and multiple cells may therefore be beneficial. Moreover, given the rare event that more than two calls are queued in a specific cell, it could be sufficient to have low and high priority calls only and this approach could also be investigated further, as well as analytical solutions of the operator specific blocking probability (with proper approximations). In the assessment of data rates with national roaming, we did not consider trunking efficiency (which increases as a function of total system capacity). As further work the capacity gain with national roaming at a given blocking probability could be thus evaluated as a function of inter-operator site distance. A related aspect, which may seem attractive at a first glance but which we do not recommend for future work, is load balancing between cellular operators that allow for national roaming. Firstly, the additional gain should be relatively small 1. Secondly, as we elaborated on in [31], the administrative overhead between cooperating operators has to be minimized. 2 A more relevant technical problem would instead be how to design neighbor cell lists for handover measurements when multiple networks are accessible (to avoid exhaustive searches, which decrease measurement accuracy). 1 In cellular systems, connecting to the BS with lowest path loss is most often sufficient. 2 Notice also that the trunking gain obtained by having access to more channels is present also without advanced load balancing schemes, since it is sufficient if users can be redirected to other operators at congestion.

74

75 References [1] François Loizillon, Jarmo Harno, Kjell Stordahl, Ilari Welling, Kjell-Ove Kalhagen, Borgar Olsen, Dimitris Varoutas, Dimitris Katsianis, Jesus Roy, José-Pedro Borrego, Daniel Martins, and Amaro de Sousa. Final results on seamless mobile IP service provision economics. Available: October IST TONIC Deliverable 11. [2] Internet world stats, usage and population statistics. Available: Accessed May [3] Kjell Stordahl (editor). Overview of demand forecasts for the fixed and mobile networks and services in Europe. Deliverable 2, October Available: [4] Qualcomm. The economics of mobile wireless data. Available: mobile data.pdf, [5] Eli Noam. Interconnecting the Networks of Networks. MIT Press, [6] Martin E. Cave, Sumit K. Majumdar, and Ingo Vogelsang. Handbook of Telecommunications Economics. Elsevier Science, 1st edition, September [7] Northstream AB. Operator options beyond 3G. Whitepaper, February Available: [8] Morten Falch and Reza Tadayoni. Economic versus technical approaches to frequency management. Telecommunications Policy, 28: , Elsevier. [9] Ronald Coase. The Federal Communications Commission. Journal of Law and Economics, (2), October [10] David P. Reed. Putting it all together: The cost structure of personal communications services. Technical report, Federal Communications Commission, Office of Plans and Policy, November OPP Working Paper No

76 60 References [11] Robert Pindyck and Daniel Rubinfeld. Microeconomics. Prentice Hall, 5th edition, [12] Analysis, DotEcon, and Hogan & Hartson. Study on conditions and options in introducing secondary trading of radio spectrum in the European Community. Available: May [13] Fredrik Berggren, Olav Queseth, Jens Zander, Börje Asp, Christian Jönsson, and Peter Sten. Dynamic Spectrum Access phase 1: Scenarios and research challenges. Technical Report TRITA-S3-RST-0407, Royal Institute of Technology, September [14] Office of Communications (Ofcom). [15] Post & Telestyrelsen (PTS). [16] Werner Mohr, Rainer Lüder, and Karl-Heinz Möhrmann. Data rate estimates, range calculations and spectrum demand for new elements of systems beyond imt In Proc. IEEE International Symposium on Wireless Personal Multimedia Communications (WPMC), volume 1, pages 37 46, October [17] Sami Uskela. Key Concepts for Evolution Toward Beyond 3G Networks. IEEE Wireless Communications, 10(1):43 48, February [18] Jens Zander. On the cost structure of future wideband wireless access. In Proc. IEEE Vehicular Technology Conference (VTC), volume 3, pages , May [19] Anders Furuskär. Radio Resource Sharing and Bearer Service Allocation for Multi-Bearer Service, Multi-Access Wireless Networks. PhD thesis, Royal Institute of Technology (KTH), April TRITA-S3-RST [20] Eva Gustafsson and Annika Jonsson. Always best connected. IEEE Wireless Communications, 10(1):49 55, February [21] Harri Honkasalo, Kari Pehkonen, Markku T. Niemi, and Anne T. Leino. WCDMA and WLAN for 3G and beyond. IEEE Wireless Communications, pages 14 18, April [22] Juha Kalliokulju, Paul Meche, Mikko J. Rinne, Jari Vallström, Prabodh Varshney, and Sven-Gustav Häggman. Radio access selection for multistandard terminals. IEEE Communications Magazine, 39(10): , October [23] Klas Johansson, Anders Furuskär, Peter Karlsson, and Jens Zander. Relation between base station characteristics and cost structure in cellular systems. In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), volume 4, pages , September 2004.

77 References 61 [24] Anders Furuskär, Klas Johansson, and Magnus Almgren. An infrastructure cost evaluation of single- and multi-access networks with heterogeneous traffic density. In Proc. IEEE Vehicular Technology Conference (VTC Spring), May [25] Klas Johansson and Anders Furuskär. Cost efficient capacity expansion strategies using multi-access networks. In Proc. IEEE Vehicular Technology Conference (VTC Spring), May [26] Klas Johansson. On the cost efficiency of user deployed access points integrated in mobile networks. In Proc. RadioVetenskap och Kommunikation (RVK), June [27] A. Bartlett and N. N. Jackson. Network planning considerations for network sharing in UMTS. In Proc. IEEE International Conference on 3G Mobile Communication Technologies, pages 17 21, May [28] Claes Beckman and Gregory Smith. Shared networks: Making wireless communication affordable. IEEE Wireless Communications, 12(2):78 85, April [29] Jarmo Harno. 3G business case successfulness within the constraints set by competition, regulation and alternative technologies. In Proc. Federation of Telecommunications Engineers of the European Community congress (FITCE), pages , September [30] Niklas Agevik. Shared UMTS networks a techno-economic study on mobile network operators possibilities to deliver unique products and services. Master s thesis, Royal Institute of Technology, April [31] Johan Hultell, Klas Johansson, and Jan Markendahl. Business models and resource management for shared wireless networks. In Proc. IEEE Vehicular Technology Conference (VTC-Fall), volume 5, pages , September [32] Klas Johansson, Martin Kristensson, and Uwe Schwarz. Radio resource management in roaming based multi-operator WCDMA networks. In Proc. IEEE Vehicular Technology Conference (VTC Spring), volume 4, pages , May [33] Johan Hultell and Klas Johansson. An estimation of the achievable user throughput with national roaming. Submitted to IEEE Vehicular Technology Conference 2006 Spring, September [34] Klas Johansson, Jan Markendahl, and Per Zetterberg. Relaying access points and related business models for low cost mobile systems. In Proc. Austin Mobility Roundtable, March 2004.

78 62 References [35] Klas Johansson, Jonas Lind, Miguel Berg, Johan Hultell, Niklas Kviselius, Jan Markendahl, and Mikael Prytz. Integrating user deployed local access points in a mobile operators network. In Proc. Wireless World Research Forum Meeting (WWRF #12), Toronto, November [36] HiQ Data AB. Potential value of additional spectrum for a UMTS operator. Available: April [37] Alex Hills and Hung Yao Yeh. Spectrum use and carrier costs: a critical trade-off. Telecommunications Policy, 23: , Elsevier Science. [38] Dan Avidor, Dan Furman, Jonathan Ling, and Constantinos Papadias. On the financial impact of capacity-enhancing technologies to wireless operators. IEEE Wireless Communications Magazine, 10(4):62 65, August [39] Florian Graf, Holger Ruse, and Jörg Krüger. Introduction scenarios for capacity enhancement techniques in GSM mobile radio networks technical potentials and economic efficiencies. In Proc. IEEE International Conference on Personal Wireless Communications (ICPWC), pages , [40] Fernando J. Velez and Luis M. Correia. Optimisation of mobile broadband multi-service systems based in economics aspects. Wireless Networks, 9(5): , Kluwer Academic Publishers. [41] Richard A. Stanley. Modeling and optimizing wireless network infrastructure economic cost. PhD thesis, Worcester Polytechnic Institute, December [42] Joseph Sarnecki, C. Vinodrai, Alauddin Javed, Patrick O Kelly, and Kevin Dick. Microcell design principles. IEEE Communications Magazine, 31(4):76 82, April [43] A. G. Spilling, A. R. Nix, M. A. Beach, and T. J. Harrold. Self-organisation in future mobile communications. IEEE Electronics & Communication Engineering Journal, 12(3): , June [44] Wei Yuan, Sairam Subramanian, and Payam Maveddat. An effective algorithm for access wireless network cost optimization. In Proc. IEEE Vehicular Technology Conference (VTC), volume 2, pages , May [45] Kjell Stordahl and Eddie Murphy. Forecasting long-term demand for services in the residential market. IEEE Communications Magazine, 33(2):44 49, February [46] Joakim Björkdahl and Erik Bohlin. Financial analysis of the Swedish 3G licensees Where are the profits? In Proc. ITS 12th European Regional Conference, September 2001.

79 References 63 [47] Kaj Thomsson. Large scale deployment of public Wireless LANs A feasibility study. Master s thesis, Royal Insititute of Technology (KTH), Stockholm, February [48] Bezalel Gavish and Suresh Sridhar. Economic aspects of configuring cellular networks. Wireless Networks, 1(1): , Kluwer Academic Publishers. [49] Dragana Kristic and Luis M. Correia. Optimisation of micro-cellular regions by minimising the number of base stations. In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), volume 1, pages 84 88, [50] Livio Santos, Lucio Studer Ferreira, Valentina Garcia, and Luis M. Correia. Cellular planning optimisation for non-uniform traffic distributions. In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), volume 3, pages , September [51] Borgar T. Olsen, Alcibiade Zaganiaris, Kjell Stordahl, L. Aa. Ims, D. Myhre, T. Øverli, M. Tahkokorpi, I. Welling, M. Drieskens, J. Kraushaar, J. Mononen, M. Lahteenoja, S. Markatos, M. De Bortoli, U. Ferrero, M. Ravera, S. Balzaretti, F. Fleuren, N. Gieschen, M. De Oliveira Duarte, and E. de Castro. Techno-economic evaluation of narrowband and broadband access network alternatives and evolution scenario assessment. IEEE Journal on Selected Areas in Communications, 14(6): , August [52] Paul E. Green. Fiber to the home: The next big broadband thing. IEEE Communications Magazine, 42(9): , September [53] Timo Smura. Techno-economic analysis of IEEE a-based fixed wireless access networks. Master s thesis, Helsinki University of Technology, April [54] David P. Reed. The cost structure of personal communication services. IEEE Communications Magazine, 31(4): , April [55] Aurelian Bria, Maxime Flament, Fredrik Gessler, Olav Queseth, Rickard Stridh, Matthias Unbehaun, Jiang Wu, and Jens Zander. 4th generation wireless infrastructures - scenarios & research challenges. IEEE Personal Communications Magazine, 8(6):25 31, Dec [56] Jorge M. Pereira. Fourth generation: now, it is personal! In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), volume 2, pages , September 2000.

80 64 References [57] Matthias Unbehaun. On the Design and Deployment of Low-cost Wireless Infrastructure. PhD thesis, Royal Institute of Technology (KTH), October TRITA-S3-RST [58] Octavian Tirla, Alejandro Bascuñana Muñoz, Peter Schoo, and Serge Tessier. Accounting management in heterogenous mobile access networks: the MIND approach. In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), volume 3, pages , September [59] Alex Hills. Large-scale wireless LAN design. IEEE Communications Magazine, 39(11):98 107, November [60] Alex Hills and David B. Johnson. Seamless access to multiple wireless data networks: A wireless data network infrastructure at Carnegie Mellon University. IEEE Personal Communications, 3(1):56 63, February [61] Harri Holma and Antti Toskala. WCDMA for UMTS: Radio Access For Third Generation Mobile Communications. John Wiley & Sons, third edition, [62] Mikael Gundmundson. Correlation for shadow fading in mobile radio systems. Electronics Letters, 29, [63] Uwe Gotzner, Andreas Gamst, and Roland Rathgeber. Spatial traffic distribution in cellular networks. In Proc. IEEE Vehicular Technology Conference (VTC), volume 3, pages , May [64] US Census Bureau. Table GCT-PH1, population, housing units, area, and density: Available: [65] Navid Rostam Khesal. Differentiation strategies for operators sharing UMTS networks. Master s thesis, Royal Institute of Technology, March [66] Third Generation Partnership Project (3GPP). Service aspects and requirements for network sharing (release 6). Technical Report , Technical Specification Group Services and System Aspects (TSG SA), December Version [67] J. A. Village, K. P. Worrall, and D. I. Crawford. 3G shared infrastructure. In Proc. IEE 3G Mobile Communication Technologies, May [68] Oriol Sallent (editor). Report on the evaluation of RRM/CRRM algorithms. Available: April EVEREST IST

81 References 65 [69] Maran Kumar Pereirasamy, Jijun Luo, Markus Dillinger, and Christian Hartmann. An approach for inter-operator spectrum sharing for 3G systems and beyond. In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), volume 3, pages , September [70] Maran Kumar Pereirasamy, Jijun Luo, Markus Dillinger, and Christian Hartmann. Dynamic inter-operator spectrum sharing for UMTS FDD with displaced cellular networks. In Proc. IEEE Wireless Communications and Networking Conference (WCNC), volume 3, pages , March [71] Third Generation Partnership Project (3GPP). Shared network support in connected mode (release 5). Technical Report TR3.012, Technical Specification Group Radio Access Network (TSG RAN), September Version [72] Nilo Casimiro Ericsson. Revenue the criterion. In Proc. Personal Computing and Communication (PCC) Workshop, November [73] Emmanuel Marilly, Olivier Martinot, Stéphane Betgé-Brezetx, and Gérard Delégue. Requirements for service level agreement management. In Proc. IEEE Workshop on IP Operations and Management, pages 57 62, [74] Telecommunication Management Forum. SLA Management Handbook, June TMF GB917. [75] Ng Chee Hock. Queueing Modelling and Fundamentals. John Wiley & Sons, [76] Clayton M. Christensen. The Innovator s Dilemma. Harvard Business School Press, [77] Geoffrey A. Moore. Crossing the Chasm. HarperCollins, 3rd edition, [78] COST 231. Digital mobile radio towards future generation system. Available: Final Report. [79] European Telecommunication Standard Institute (ETSI). Selection procedures for the choice of radio transmission technologies of the UMTS (UMTS 30.03). Technical Report TR , April Version [80] John G. Proakis. Digital Communications. McGraw-Hill, 4th edition, [81] Eurescom. Investment analysis modelling (deliverable 2). Available: August Project P901-PF, Deliverable 2. [82] Martha Amram and Nalin Kulatilaka. Real Options: Managing Strategic Investment in an Uncertain World. Oxford University Press, December 1998.

82 66 References [83] Helmut Meier, Roman Friedrich, Hanno Blankenstein, and Alexander Froment-Curtil. How wireless carriers will make mobile data pay. Available: October [84] Helmut Meier, Roman Friedrich, Hanno Blankenstein, and Jonathan Corp. Mobile data: Creating sustainable value. Available: data.pdf, [85] Costas Courcoubetis and Richard Weber. Pricing Communication Networks: Economics, Technology and Modelling. John Wiley & Sons, [86] Thomas T. Nagle and Reed K. Holden. The Strategy and Tactics of Pricing A Guide To Profitable Decision making. Prentice Hall, 2nd edition, [87] John Hagel III and Marc Singer. Unbundling the corporation. Harward Business Review, 77(2): , March/April [88] Gunnar Almgren. Roaming between Wireless ISPs: Closed club or commons? In Proc. Stockholm Mobility Roundtable, May 2003.

83 Appendices 67

84

85 Appendix A Cost Modeling and Pricing Strategies In this appendix we first describe and motivate the cost modeling used in the infrastructure cost analysis, and further summarize a few basic pricing strategies that quite often are used by MNOs. A.1 Discounted Cash Flow Modeling It should be stressed that we deliberately have chosen a simplistic model for this initial assessment of heterogeneous wireless infrastructure costs. The interested reader is further referred to [11] for standard micro-economical cost definitions, and [81] for an excellent overview of more detailed cost modelling applied to telecommunications. 1 The purpose with our model is to include both investments and running costs. 2 This can be done in several ways, where we have chosen a method inspired by Net Present Value analysis (also referred to as Discounted Cash Flow modeling) which is widely used for rudimentary investment analysis [11]. We thus calculate the cost (per BS) in present value, with a conventional 10% discount rate. 3 In general, the discount rate (r) reflects how willing a firm is to take risks. It is thus of great importance, since it directly will affect the total net present value. For more explicit derivations of r, a model often used is the Weighted Average Cost of Capital (WACC). This may be calculated as a function of the cost of debts (C d ), tax level (T ), cost of equity (C e ) and the size of debt (D) 1 As noted in [81], cost modeling becomes quite complex when used for real investment analyses and regulation of inter-connection charges. 2 Often denoted Capital Expenditures (CAPEX) and Operational Expenditures (OPEX). 3 Here, we could as well have annualized the investments, for example with a linear depreciation during the system life cycle. 69

86 70 Appendix A. Cost Modeling and Pricing Strategies and equity (E) according to: D r = C d (1 T ) D + E + C E e D + E (A.1) As we can see from the model above the discount rate will depend on the ratio between equity and debt for the investment. Furthermore, the discount rate may very well vary as a function of time. A more detailed Net Present Value analysis thus requires detailed information on the investment to be judged. Another popular method for more advanced investment analysis, which lends itself well to the fast changing telecommunication industry [81], is the real options approach [82]. With this method, also future opportunities can be modeled explicitly. A.2 Pricing Strategies This appendix includes an overview of common pricing strategies according to micro-economic theory [11], with a few examples of applications in mobile communications. Pricing with Market Power Microeconomic theory tells that a firm acting on a perfectly competitive market, where the price can not be affected by a single firm, should produce goods until the marginal cost equals the price [11]. This way the competitive firm maximizes its profit in the short run. For firms with market power, which is the interesting case in practice, the level of output should be chosen in another way. The simplest case is the monopolistic firm, where the output level should be chosen so that marginal revenue equals the marginal cost. The monopolist will hence set the price significantly higher than the competitive price. In oligopoly markets, characterized by that a few firms produce most or all of the goods or services, some firms will earn substantial profits in the long run. This is because there are market entry barriers that make it difficult or impossible for other firms to enter. The price will be determined based on strategic decisions, but the price should be set somewhere between the price level chosen by a monopolistic and the price in a perfectly competitive market. It is also important to understand that there are a number of ways in how a firm can increase its revenue (capturing parts of the consumer surplus) through different kind of price discriminations. Price Discrimination Price discrimination is often implemented by mobile operators, for instance by marketing different subscriptions towards different subscriber groups ( block pricing ). Operators thereby charge differently per, for example, minute of use dependent on the total number of call minutes per month.

87 A.2. Pricing Strategies 71 Another standard method is to brand and price the same product differently, and market different brands towards different consumer groups. This way, users that are willing to pay more can be attracted to the brand with higher price (even though the product in principle is the same). We can see examples of this among mobile operators who have launched own MVNOs under separate brands. The objective is to be able to target low-price segment without damaging the main brand. Both inter-temporal price discriminations and peak-load pricing are very common in mobile networks. The aim with the inter-temporal price discriminations is to charge early adopters more than needed, since they have a high demand for the product and consequently may be willing to pay more. Peak load pricing is implemented in such a way that telephone calls cost more during office hours than at night time and during weekends. This is particularly important if we take into account that mobile networks have to be dimensioned for peak load (during busy hour). Value versus Volume Based Pricing The principles of value and volume based pricing in communication networks have been debated during the last years. Fixed broadband operators have focused on attaining customers by charging flat rate pricing [83,84]. Some MNOs have also use the same strategy to acquire more voice subscribers and reduce churn, in particular (so far) in the United States. Flat rate pricing for GPRS and 3G data services are normally only used by MNOs as a way of marketing new services, and to teach their customers to use and appreciate the new services. At the same time, volume based pricing should be avoided in mobile networks [83]. In volume based pricing all costs, fixed and variable, are allocated to the produced services proportional to the amount of bottleneck resource they consume [11]. At each given point in time a mobile network is limited by the number of bits available. Using this logic, each application should be priced proportionally to the number of transmitted bits [85]. However, this rapidly makes capacity demanding data services very expensive, especially since a user does typically not experience any relation between data volumes and the perceived value. Hence, volume based pricing is very problematic in mobile data networks and it will will fail to capture significant parts of the consumer surplus [11,83,86]. To increase their revenues, MNOs may instead use value-based pricing, where consumers are charged per service rather than per bit (e.g. 1 Euro per song), as suggested in [83,84]. In practice, though, it is rare that only one pricing strategy is used. This is simply because pricing in practice is too complex and depend various controllable and uncontrollable variables [86].

88

89 Appendix B Network Sharing Use Cases Infrastructure sharing between multiple operators can implemented in several ways [1, 27, 30, 67]. Physical infrastructure, such as masts and antenna systems, have been shared quite frequently already in second generation systems. Sharing the complete RAN and part of the core network has just recently been implemented in some countries during the roll-out of 3G networks. These network sharing methods are of course suitable for different use cases and, in summary, the following flavors of network sharing are commonly referred to in the literature: Site sharing Only non-intelligent equipment at base station sites are shared. For example masts and power supplies, possibly also antenna systems. BS sharing The BSs (and below ) are shared, but operators have their own radio network controllers and core networks. RAN sharing The whole RAN is shared, but core networks are still operator specific. Common Shared Network Both the RAN and parts of the core network is shared between operators. Geographical Sharing Operators agree to build and operate geographically split networks, but allow for roaming for each others users. MVNO Operators without own spectrum licenses and RANs, who offer mobile services. While the first five sharing solutions refer to network sharing between MNOs as a means to reduce cost in areas where the networks have significant overcapacity, the latter (MVNO) is driven by other factors which will be discussed next. 73

90 74 Appendix B. Network Sharing Use Cases B.1 Mobile Virtual Network Operators MVNOs have recently been identified by the national telecom regulators and competition authorities as a means to, similar to the unbundling of the local-loop for DSL services, increase competition also in the oligopoly like mobile operator market. There are different types of MVNOs, with different background and competitive strategy. Most common today are branding MVNOs, fixed line telephony and broadband service providers, and mobile telephony operators targeting specific market segments. In a wider perspective, the scope of the telecom operators business is since a few decades ago constantly undergoing a change [6]. Today, we see that new roles are developed in the industry. Driven by the development towards a diverse portfolio of services, MNOs tend to focus more on developing and marketing user applications and services. At the same time, telecom equipment vendors seem to seize the opportunity to integrate upwards in the value chain and offer network operations and service platforms to the operators. A separation between infrastructure, product innovation, and customer relationship businesses could often be beneficial from an organizational point of view and stimulate innovation of new services [87]. However, this does not imply that MVNOs are best operated as small businesses. On the contrary, driven by economies of scope and a strive to offer each customer as many services as possible, customer relationship business tend to benefit from size [87]. 1. Hence, it is plausible that the role as service provider and intermediator for specialized producers of content will be important both for MVNOs and MNOs in the future. So far, however, with voice services as the key offering, the business case of telecom MVNOs have relied on sufficiently low wholesale cost of network access [27]. Even though regulators are well aware of that, and enforce cost based inter-connection charges, it is of course difficult for MVNOs to have a cost advantage towards the MNOs for the access. Successful MVNOs therefore need other strategic advantages, such as a strong brand, an existing customer base (that are interested in the service), a streamlined customer care organization, or a niche service that the traditional MNOs neither could nor would offer [65]. 2 Of all these, the two most important drivers for MVNOs today, and during the foreseeable future, will therefore probably be their opportunities to target niche services and customer segments [28,29,65,72]. 1 Infrastructure business such as access provisioning also benefit from size, but instead due to economies of scale [87]. 2 For a win-win situation, the MVNO and hosting MNO should not compete for the same customers.

91 B.2. Inter-Operator Charging with Roaming Based Sharing 75 In this context, it should also be noted that, as emphasized in [30], the network configuration need to be tailored for many mobile services (in terms of QoS, area coverage, billing, etc.). Smaller MVNOs with little bargaining power will consequently need to adapt their service offering to the specific capabilities of the network. Hence, with only a few networks available, which most often are optimized according to different criteria (depending on the business model of the respective MNO), and the strategic considerations outlined above, it may very well be so that a specific MVNO do not have many viable options of network providers to choose between. B.2 Inter-Operator Charging with Roaming Based Sharing As part of the GSM standard, international roaming has been extremely profitable for mobile operators, and a common assumption is that approximately 15% of their revenues originate from roaming calls [88]. GSM operators today sign bilateral roaming agreements with license holders, but typically not with MVNOs, from other countries. Hence, MVNO subscribers are referred to the roaming partners of their respective hosting network operator. Accounting between operators is handled via international clearing houses. As we are used to, subscribers normally pay for the international part of the call. The home operator, however, may have a discount with the visited operator and is then charged less per voice minute than what see on the bill [88]. The core network functionalities originally intended for billing, service provisioning, and mobility management with international roaming are naturally applicable also for roaming based sharing.

92

93 Part II Paper Reprints 77

94

95 Chapter 5 Relation Between Base Station Characteristics and Cost Structure in Cellular Systems (Paper 1) Klas Johansson, Anders Furuskär, Peter Karlsson, and Jens Zander, In Proc. IEEE PIMRC 2004, October

96

97 81 RELATION BETWEEN BASE STATION CHARACTERISTICS AND COST STRUCTURE IN CELLULAR SY STEM S Klas Johansson 1, A nders F u ru sk är 2, P eter Karlsson 3, and Jens Z ander 1 1 W ireless@ KT H, E lec tru m 4 1 8, S Kista, S weden, {k lasj, jens.z ander}@ radio.k th.se 2 E ric sson A B, S E S toc k holm, S weden, anders.fu ru sk ar@ eric sson.c om 3 T elias onera S weden, B ox 9 4, S E M alm ö, S weden, P eter.c.karlsson@ teliasonera.c om Ab str a c t - A sim p le m ethod for estim ating the c osts of b u ilding and op erating a c ellu lar m ob ile network is p rop osed. U sing em p iric al data from a third g eneration m ob ile sy stem (W C D M A ) it is shown that the c ost is driv en b y different fac tors dep ending on the c harac teristic s of the b ase stations dep loy ed. W hen site density inc rease, op erational and transm ission c osts tend to dom inate rather than radio eq u ip m ent and site c osts. T he resu lts also show how, for different c ap ac ity req u irem ents, the c osts c an b e m inim iz ed b y a p rop er selec tion of for ex am p le m ac ro, m ic ro and p ic o b ase stations. In m any sc enarios the m ac ro b ase stations y ield the lowest c ost, indic ating that c ov erag e (c ell rang e) is an im p ortant p aram eter when desig ning wireless sy stem s. K e y w o r d s - T ele-ec onom ic s, c ost m odel, infrastru c tu re c ost, b ase station c ost I. I N T R O D U C T I O N T he c osts of p rov iding wide-area c ov erag e for hig h data rate wireless ac c ess hav e b een disc u ssed widely in the telec om indu stry ov er the last c ou p le of y ears. W hile m ob ile op erators hav e stru g g led with hig h lic ense fees and rollou t c osts du e to reg u latory req u irem ents for third g eneration network s, tec hnolog ies su c h as W ireless L A N hav e ev olv ed as c om p lem enting and in sp ec ifi c sc enarios ev en c om p etitiv e alternativ es [2 ]. T his has ev idently c ontrib u ted to an inc reased c ost awareness am ong b oth m ob ile op erators and eq u ip m ent v endors [4 ]. M ob ile infrastru c tu re c ost was u nder stu dy already du ring the dev elop m ent of G S M and other 2 G sy stem s, howev er it has b een m ore foc u sed only rec ently for 3 G and b ey ond. In [6 ] the c osts of p rov iding m ob ile data serv ic es was analy z ed in term s of ec onom ies of sc ale and sc op e. F u rtherm ore, c ost effec tiv e way s of c onfi g u ring c ellu lar network s was addressed in [1 ] and an em p iric ally b ased c ost m odel for c ellu lar sy stem s was p rop osed in [7 ]. It is c om m only k nown that the c ap ac ity is p rop ortionally to the b ase station density for a g iv en c ellu lar sy stem. U nfortu nately, also the infrastru c tu re c osts seem s to inc rease alm ost linearly with the c ap ac ity req u ired (indic ating a low deg ree of ec onom ies of sc ale). T his was disc u ssed in [9 ], where the c ost stru c tu re of wireless ac c ess infrastru c tu re was analy z ed. It was c onc lu ded that the network c ost rises linearly with the data rate p er u ser. T his shou ld hold for a g iv en freq u enc y alloc ation p rov ided that the sam e c ov erag e is req u ired, and was identifi ed in [1 0 ] as a k ey p rob lem for p rov iding wideb and data serv ic es in wireless sy stem s. A sim p le infrastru c tu re c ost m odel was also p resented in [9 ] (and dev elop ed fu rther in [1 0 ]), in whic h the total infrastru c tu re c ost of a wireless sy stem is m odeled as linearly p rop ortional to the nu m b er of b ase stations: C system = cn b s, (1 ) where N b s is the nu m b er of b ase stations and c is a c onstant c orresp onding to the c ost p er b ase station. N ote that in [9 ] c is assu m ed to b e the sam e for all b ase stations and it is indep endent of the b ase station c harac teristic s. H owev er, in a p rac tic al sy stem, a nu m b er of different b ase station ty p es c ou ld b e u sed for different dep loy m ent sc enarios and the req u irem ents on, e.g., c ell rang e and reliab ility g reatly affec ts the total c ost p er b ase station (inc lu ding c ap ital and op erational ex p enditu res). A s a c onseq u enc e the c ost stru c tu re of a radio ac c ess network is dep endent of the sy stem c onfi g u ration, i.e. the q u antities and ty p es of different ac c ess p oints em p loy ed to ac hiev e v ariou s total network c ap ac ities and c ov erag e. T his top ic will b e treated fu rther in the seq u el of this p ap er, whic h is ou tlined as follows. T he ov erall distrib u tion of c osts in c u rrent m ob ile network s is p resented in S ec tion II. T his disc u ssion ju stifi es a sim p le infrastru c tu re c ost m odel whic h is desc rib ed in S ec tion III. U sing this, the total c ost and c ost stru c tu re is c alc u lated in S ec tion IV for different dem ands and p otential solu tions for deliv ering affordab le wireless serv ic es with hig h data rates is disc u ssed b riefl y. T he p ap er is c onc lu ded in S ec tion V. I I. O V E R A L L C O S T S T R U C T U R E F O R M O B I L E O P E R A T O R S B efore g oing into a m ore detailed analy sis, let u s fi rst look b riefl y at the ov erall c ost stru c tu re of a m ob ile op - erator as of today. T y p ic ally, their inv estm ents relate to radio and transm ission eq u ip m ent, lic ense fees, site b u ildou ts and installation of eq u ip m ent. T he ru nning c osts, in tu rn, c onsider m ainly transm ission, site rentals, m ark eting, term inal su b sidies, and op eration and m aintenanc e (O & M ). T he ex ac t b reak down of those c osts is of c ou rse c ase sp ec ifi c, and it m ay v ary sig nifi c antly b etween different

98 82 Chapter 5. Base Station Characteristics (Paper 1) countries and operators. An attempt to model the costs and rev enues for w estern E uropean operators w as done w ithin the T O N IC project [5 ]. T he cost structure w as estimated for operators prov iding U M T S serv ices in (i) a small and sparsely populated country and (ii) a larg e country w ith denser population. F rom these results it is clear that cumulated running costs dominate the total cost structure of a mob ile operator and, according to [5 ], they correspond to roug hly 7 5 % of the total costs for a larg e country. M ore specifi cally, the running costs are dominated b y non-technical costs, such as mark eting, terminal sub sidies and w ag es w hich can b e seen in T ab le 1. N ote also that transmission constitutes a sig nifi cantly hig her portion of the running costs in the small country. T his since last mile -transmission is priced per k ilometer in the model in [5 ] and the distance b etw een b ase stations is hig her in the small country ex ample, due to its low er population density. T ab le 1 T ypical running cost structure (b ased on [5 ]). Large country Small country S ite rental 5 % 5 % T ransmission 5 % 2 5 % T erminal sub sidies 1 5 % 1 5 % M ark eting 2 5 % 1 5 % E mployees 5 0 % 4 0 % Another interesting, althoug h perhaps not surprising, fact is that the v ast part of the infrastructure related costs stems from the radio access netw ork (including radio netw ork controllers, b ase stations, sites, and last-mile -transmission). C ore netw ork eq uipment such as b ack b one transmission, sw itches, routers, charg ing functionality, and sub scrib er reg - isters only contrib ute to % of the ov erall netw ork costs. T his is in particular clear from the inv estment cost structure presented in T ab le 2. T ab le 2 O perator inv estment structure (b ased on [5 ]). Large country Small country C ore netw ork 3 0 % 1 0 % S ite b uildout 3 0 % 5 0 % R adio access netw ork 4 0 % 4 0 % I I I. C O S T E S T I M AT I O N M E T H O D AN D AS S U M P T I O N S After the discussion ab ov e, it seems reasonab le to limit the infrastructure cost model to the radio access netw ork. T hus, only b ase station eq uipment, site costs and last mile - transmission are included and w e use a W C D M A system as a case study. T he same cost model as g iv en in (1 ) can then b e used, b ut it remains to determine the total cost per b ase station c. H ow ev er, to mak e the model more realistic w e need to address that an operator seldom has the same b ase stations for ev ery scenario. A. Infrastructure cost model In principle there is an infi nite numb er of possib le confi g urations of b ase stations (including different alternativ es for sites, transmission, etc.). A roug h div ision, thoug h, could b e to stick to three main categ ories b ased on the cell rang e; namely macro, micro and pico cell b ase stations. T he cost per b ase station c should also b e sig nifi cantly different for those b ase stations. F or ex ample, a small micro or pico b ase station implies a low cost for eq uipment, site leases and installation w hereas a larg e macro b ase station costs much more in those aspects. O n the other hand, fi x ed costs not directly related to the capacity of the b ase station are div ided b etw een many users in a macro b ase station so the cost per user may still b e low er in many scenarios. T he total infrastructure cost for a mob ile operator could then b e modeled as C system = c 1 N ma c r o + c 2 N mic r o + c 3 N p ic o, (2 ) w here c 1, c 2, and c 3 are the total costs for macro, micro and pico b ase station respectiv ely. T ypically c 1 > c 2 > c 3 and, if w e in the same w ay defi ne the max imum cell radius R per b ase station, R 1 > R 2 > R 3. H ence, different b ase stations w ill minimiz e cost for different scenarios. H ow ev er, for the sak e of simplicity, w e w ill study the different b ase stations separately b ut k eep in mind that each cellular netw ork in reality consist of a mix of b ase stations. B. N etw ork dimensioning T he numb er of b ase stations req uired, N b s, is calculated as a function of the demand specifi ed b y the: S erv ice area to b e cov ered, A ser v ic e. Av erag e capacity per user during b usy hour, W u ser. N umb er of sub scrib ers w ithin the cov erag e area, N u ser. F urthermore, the dimensioning w ill b e done for dow nlink only. T his should b e reasonab le since the dow nlink g enerally limits the ag g reg ate capacity in a W C D M A system, w hile the uplink limits the data rate per link and cov erag e w hen the traffi c load is low [3 ]. O nly a sing le carrier is assumed, to mak e the comparison b etw een b ase station types simple (more carriers w ould reduce the numb er of sites in a capacity limited scenario). E ach b ase station has a g iv en max imum cell rang e (R ma x ), minimum cell rang e (R min ) and supported capacity (W ma x ). F or simplicity the capacity is k ept constant, and does not v ary as a function of the actual cell rang e (R b s ). E ach cell has circular cov erag e area, w hich according to [7 ] is a reasonab le assumption. T he netw ork can either b e cov erag e or capacity (interference) limited and the numb er of b ase stations req uired is dimensioned according to the follow ing model: { Aser v ic e N b s = m a x π Rma 2, N } u ser W u ser, (3 ) x W ma x

99 83 assuming a continuous service area and that users are uniformly distrib uted (as in e.g. [9 ]). N ote also that the netw ork is dimensioned in an average sense, and that the w anted b lock ing and outage p rob ab ility has to b e p ossib le to achieve at the assumed cell range R max and b ase station cap acity W max for each considered service. N ow, given that the resulting cell range As e r v ic e R b s = R min, (4 ) π N b s the cap acity req uirements can b e met w ith the selected ty p e of b ase station. C. Empirical cost and performance data T he p erformance and cost data related to the b ase stations are given in T ab le 3. A ll values are ap p rox imate, b ut should b e rep resentative in a relative sense for a ty p ical W C D M A sy stem dep loy ed during F or simp licity only an urb an scenario is considered and ty p ical cell ranges and cap acities are b ased on general estimates p rovided in [3 ] and do not rep resent the p erformance of any sp ecifi c p roduct. W e assume that the cap acity p er cell is higher for micro and p ico cell b ase stations. T his since it is p ossib le to minimiz e inter-cell interference b y a p rop er p lacement of the antennas (b elow roof-top or indoors). T he eq uip ment costs estimates have b een p rovided b y the G artner G roup and the other cost p arameters are b ased on [5 ]. T he macro b ase station is naturally much more ex p ensive than the smaller b ase stations, b ecause of its higher outp ut p ow er and cap acity, b ut also due to that it has to b e more reliab le since more users are served p er b ase station. T his clearly affects the costs for sites, installation and O & M. T ab le 3 B ase station p erformance and costs Macro BS Micro BS Pico BS P erformance [ 3 ] : S ectors C arriers (2 * 5 M hz ) M ax imum cell range (R max) 1 k m k m 0.1 k m M inimum cell range (R min ) k m 0.1 k m k m C ap acity (W max) M b p s M b p s M b p s Initial costs: E q uip ment (S ource: G artner) 5 0 k e 2 0 k e 5 k e S ite b uildout [5 ] 7 0 k e - - S ite installation [5 ] 3 0 k e 1 5 k e 3 k e A nnu al costs [ 5 ] : A nnual O & M 3 k e 1 k e 1 k e S ite lease 1 0 k e 3 k e 1 k e T ransmission 5 k e 5 k e 5 k e L ast mile -transmission costs are also included in the model, and for this p urp ose w e use a simp lifi ed modeling of leased lines. E ach b ase station is assumed to have the same cost for transmission of 5 k e p er y ear. T he transmission p rices are sub ject to a y early p rice erosion of 5 %. T he other annual costs are assumed to b e constant. D. D iscou nted cash fl ow model T he total cost p er b ase station c is calculated in p resent value using a standard economical method for cumulated discounted cash fl ow s. B y doing so, w e can account for b oth investments and running cost in the comp arison and analy z e the total cost structure of different b ase stations. T his is simp ly done b y summing up the discounted annual cash fl ow s (in this case y early ex p enditures) for the w hole netw ork life cy cle (K y ears) according to K 1 c k c = (1 + d) k, (5 ) k= 0 w here d is a discount rate w hich is assumed to b e eq ual to 1 0 %. T he netw ork is in all ex amp les in the seq uel assumed to b e used during K = 1 0 y ears and the cost for eq uip ment and site b uildouts are accounted for in the fi rst y ear (k = 0 ). T hat is, the w hole netw ork is dep loy ed during the fi rst y ear. E. D iscu ssion on th e model s applicab ility A lthough this is a very simp lifi ed model, w e b elieve this ap p roach can b e useful to understand the fundamental characteristics of different technical solutions. E.g., w hen different b ase stations are ap p licab le and w hat the b ottleneck s are in today s sy stem. Y et, a few imp ortant things that distinguish the model from a real netw ork could b e w orth to p oint out. F irstly, neither b ase stations, nor users, are uniformly distrib uted as w e assume in the simp le model. S econd, a netw ork ty p ically consists of a variety of b ase stations, transmission and antenna sy stem designs. T he latter is p artly due to that an op erator naturally op timiz es the choice of technology dep ending on the sp e- cifi c dep loy ment case as discussed ab ove. B ut also b ecause netw ork s evolve over time, and ex isting infrastructure can q uite often b e reused w hen new technology is rolled out. H ence, the p revious investments in, e.g., sites and cab ling are treated as long term investments and do not add to the incremental cost of adding more cap acity. N ote also that the emp irical cost and p erformance data only should b e considered as estimates, and the statistical signifi cance of those is not k now n. H ow ever, w e b elieve that the fi gures used refl ects the costs for a ty p ical mob ile netw ork fairly w ell and the conclusions should therefore hold also for other cellular technologies, such as G S M or C D M A I V. R E S U L T S In this section w e w ill illustrate how the total sy stem cost varies as a function of demand, given b y the user density and demanded b usy hour throughp ut p er user W u s e r, and the b ase station ty p e (macro, micro and p ico). T his is follow ed b y an analy sis of the cost structure of the b ase stations under study, and a b rief discussion on technical imp rovements req uired to p rovide high data rates w ith w ide area coverage.

100 84 Chapter 5. Base Station Characteristics (Paper 1) 10 4 A v e ra g e b u s y h o u r th ro u g h p u t p e r u s e r W u s e r = 1k b p s in p re s e n t v a lu e [k E u ro ] In fra s tru c tu re c o s t p e r k m P ic o B S M ic ro B S M a c ro B S C a p a c ity lim ite d C o v e ra g e lim ite d U s e rs /k m 2 Fig. 1. Infrastructure cost for different base stations. C o s t p e r u s e r in p re s e n t v a lu e [k E u ro ] 10 0 W = 10k b p s u s e r W u s e r = 1k b p s 10 1 W = 0.2k b p s u s e r U s e rs /k m 2 Fig. 2. M inim um cost p er user for different cap acity. A. Infrastructure cost Fig. 1 illustrates th e total infrastructure cost C system for different base stations w ith a data rate W u ser = 1k bp s. T h is could, e.g., corresp ond to a sp eech serv ice of 10 k bp s at 0.1E rlang traffi c load (ty p ical corp orate user). A ccording to (3 ) th e infrastructure cost is constant as long as th e sy stem is cov erage lim ited. T h en, as m ore base stations are needed to m eet th e cap acity req uirem ents th e total cost increase linearly according to th e cost p er base station c. From th is p icture it is p ossible to fi nd th e base station ty p e th at m inim iz e infrastructure cost for different user densities. In th is sp ecifi c case, w ith W u ser = 1k bp s, th e m acro cell base stations sh ould be used until dem and ex ceed users/k m 2, th ereafter m icro cell base stations could be w orth w h ile to introduce up to a v ery h igh user density (ap - p rox im ately users/k m 2 ) w h en p ico cells are ch eap est. W e can also see w h en dem and increase signifi cantly so th at a denser dep loy m ent is needed, th e cost p er user is low ered and th ere is a certain degree of scale econom ics as dep icted in Fig. 2. H ere w e h av e p lotted th e cost p er user as function of user density for W u ser eq ual to 0.2 k bp s (ty p ical p riv ate sp eech user at 2 0 m E rl), 1k bp s (ty p ical corp orate sp eech user at 10 0 m E rl), and 10 k bp s (data user dow nloading ap p r. 5 M B /h our) resp ectiv ely. N ote also th at th e cost p er user dim inish es step w ise because of th e lim ited num ber of base station ty p es used in th is ex am p le and th at each base station is cov erage lim ited w ith in som e region. B. C ost structure T h e cost structure of different solutions is h ere div ided in th ree p arts: R adio: B ase station eq uip m ent and discounted O & M costs. S ites: S ite buildout & installation and discounted site leases. T ransm ission: discounted lease line costs. T h e total cost p er base station c and th e resp ectiv e cost structure are giv en in T able 4. T h e v alues are based on th e assum p tions giv en in S ection III. T able 4 C ost structure w ith different base stations. Base station R ad io S ites T r ansm ission C ost p er BS M acro 7 0 k e 16 8 k e 2 8 k e e M icro 2 7 k e 3 5 k e 2 8 k e 9 0 k e P ico 12 k e 10 k e 2 8 k e 5 0 k e In th is ex am p le m icro base stations are 6 6 % ch eap er th an m acro base stations, w h ereas th e cost for a p ico base station is only 4 4 % low er th an a a m icro base station. T h is h as a rath er intuitiv e ex p lanation: th e eq uip m ent cost is low er and site costs can be reduced signifi cantly as th e req uired cell range decreases. H ow ev er, th e transm ission costs are th e sam e for all base stations in th is ex am p le. H ence, as th e base station range decrease th e cost is driv en by transm ission, rath er th an by radio and site costs. C. P otential d ev elop m ent p ath s tow ard s h ig h er d ata rates at a low cost A k ey p roblem for deliv ering data serv ices in w ireless sy stem s is th e cost p er bit, w h ich does not decrease in th e sam e p ace as dem and increases w ith today s cellular tech nology. A s discussed in [9 ] w e can not assum e th at th e users total w illingness to p ay for w ireless serv ices increase signifi cantly in th e future (at least not in th e sam e order as data traffi c is ex p ected to grow ). T h erefore, nov el solutions seem to be needed in order to ach iev e greater econom ics of

101 85 scale in wireless networks. But how can this be achieved in p ractice? T here are m ainly two p ossible develop m ent tracks. O ne is to re-use ex isting sites, or even reduce the num ber of sites, by ex tending the cap acity of ex isting solutions. T his can in p rincip le be done by either 1 ) allocating m ore sp ectrum for third g eneration networks, 2 ) increase sp ectral effi ciency, e.g. by utiliz ing adap tive antennas. 3 ) introduce m ulti-hop technolog y in the cellular networks, or using a com bination of those. Another way would be to actually allow for denser dep loy m ent by decreasing the cost p er base station. U sing the cost structure analy sis above as a starting p oint, there is a p o- tential in lowering transm ission and O & M costs for p ico cell base stations. Instead of the ex p ensive leased lines (E 1 /T 1 ) or m icrowave radio links cheap er transm ission technolog ies could be introduced, e.g., wireless fi x ed broadband or x D S L. T he costs for O & M and sites could be reduced by allowing for p rivately owned and dep loy ed base stations, p ossibly connected to ex isting fi x ed broadband or local area networks. S im ilar to W ireless L AN access p oints, one could im ag ine sm all 3 G base stations owned by individuals or enterp rises. H owever, for such solutions to be econom ically feasible, som e sig nifi cant m odifi cations are req uired also in the core network eq uip m ent (sim p ly to handle a larg e increase in the num ber of base stations). It also req uires slig htly m odifi ed business m odels and value chain constellations for the m o- bile op erators. V. C O N C L U S I O N A sim p le m odel for estim ating the infrastructure costs of cellular sy stem s was p rop osed. T he m odel is based on averag e cost and p erform ance data from third g eneration m obile networks and includes both investm ents and running costs. W ith this m odel, it is p ossible to analy z e the infrastructure cost as a function of dem and for different base station confi g urations. T he cost drivers were shown to be a function of the characteristics of the base stations. W ith m acro base stations the costs m ainly considers base station eq uip m ent, O & M and sites, whereas for p ico-cell dep loy m ent the last m ile transm ission dom inates with the p resent technolog y. Results also show that the m acro base stations y ield the lowest cost in m any scenarios. T his indicates that coverag e (cell rang e) is an im p ortant p aram eter when desig ning future wireless access sy stem s. F urther studies in this area could include im p roved m odeling and m ethods for evaluating the econom ical g ain of new technical features, e.g. by m eans of elasticity analy sis. Also the p otential econom ical benefi ts and suitable business m odels for a wide dep loy m ent of p ico base stations, or other ty p es of local access p oints, could be interesting to study in m ore detail. AC K N O W L E D G M E N T T hanks to M r. J ason C hap m an (T he G artner G roup ) and D r. Jan W erding for p roviding em p irical cost data and valuable insig hts on the fi nancial asp ects of wireless networks. T he fi nancial sup p ort from the S wedish F oundation for S trateg ic Research via the Affordable W ireless S ervices and Infrastructure (AW S I) p rog ram is g reatly ap p reciated. RE F E RE N C E S [1 ] B. G avish and S. S ridhar, E conom ic asp ects of confi g uring cellular networks, Wireless Networks, V ol. 1, N o. 1, F eb , p p [2 ] J. H arno, 3 G Business C ase S uccessfulness within the C onstraints S et by C om p etition, Reg ulation and Alternative T echnolog ies, in the P roc eed in g s of th e F IT C E E u rop ea n T elec om m u n ic a tion s C on g ress, [3 ] H. H olm a and A. T oskala, WC D M A for U M T S, J ohn W iley & S ons, [4 ] D. K atsianis et al., T he econom ic p ersp ective of the m obile networks in E urop e, IE E E P erson a l C om m u n i- c a tion s M a g a z in e, V ol. 8, N o. 6, p p , D ec [5 ] F. L oiz illon et al., F in a l resu lts on sea m less m ob ile IP serv ic e p rov ision ec on om ic s, IS T T O N IC D eliverable num ber 1 1, O ct [6 ] D.P. Reed, T he C ost S tructure of P ersonal C om m u- nication S ervices, IE E E C om m u n ic a tion s M a g a z in e, V ol. 7, N o. 2, Ap r , p p [7 ] R. S tanley, A m ethodolog y for evaluating and op tim iz - ing wireless sy stem infrastructure costs, in P roc eed - in g s of th e IE E E In tern a tion a l S y m p osiu m of P erson a l, In d oor a n d M ob ile R a d io C om m u n ic a tion s (P IM R C ), [8 ] F.J. V elez, L.M. C orreia, C ost/revenue op tim isation in m ulti-service m obile broadband sy stem s, in the P roc eed in g s of th e IE E E c on feren c e on P erson a l, In d oor a n d M ob ile R a d io C om m u n ic a tion s (P IM R C ), [9 ] J. Z ander, O n the cost structure of future wideband wireless access, in p roc eed in g s of th e IE E E V eh ic u la r T ec h n olog y C on fereren c e (V T C ), [1 0 ] J. Z ander, Affordable m ultiservice wireless networks - research challeng es for the nex t decade, in P roc eed - in g s of th e IE E E In tern a tion a l S y m p osiu m on P erson a l, In d oor a n d M ob ile R a d io C om m u n ic a tion s (P IM R C ),

102

103 Chapter 6 An Infrastructure Cost Evaluation of Single- and Multi-Access Networks with Heterogeneous Traffic Density (Paper 2) Anders Furuskär, Klas Johansson, and Magnus Almgren, In Proc. IEEE VTC2005 Spring, May

104

105 89 An Infrastructure Cost Evaluation of Single-andMulti-Access Networks with Heterogeneous Traffic Density Anders Furuskär andmagnus Almgren W ireless Access Networks Ericsson Research Kista,Sweden [anders.furuskar,magnus.almgren]@ ericsson.com Abstract Traditional performance measures like capacity, cell radius and supported QoS are often insufficient when comparing wireless networks with different network architectures and cost structures.instead, in this paper, infrastructure cost is used to compare different operator deployed single- and multi-access wireless networks, including3g, W LAN and proposed 4G radio access technologies.for this purpose a model for the geographical distribution of traffic is introduced.despite the spatially nonuniform traffic demand, single-access solutions like W CDM A High-Speed Downlink Packet Access (HSDPA)or Long-Term 3G Evolved, with high capacity macro cellular base stations, typically yield the lowest costs per user.in particular this holds for a hypothetical Long-Term 3G Evolved system operating in 450M Hzspectrum, which indicates the importance of good coverage.operator deployed W LAN-only solutions are more expensive even for small fractions of supported users.m ulti-access solutions, combiningfor example W CDM A DCH or HSDPA with W LAN, do not seem to provide better cost efficiency than standard hierarchical cell structures in single-access systems.instead, multi-access solutions have to be motivated by other factors like peak data rates and spectrum availability. Keywords:Infrastructure cost,tele-economics,multi-access, WCDMA,3G,Long-Term 3G Evolution,4G,WLAN I. INTRODUCTION Mobile networkoperators are typicallyinterestedin maximizingthe profit determinedbythe revenue generatedbytheir systems andtheir costs.traditional performance measures used for single-access cellular systems,suchas coverage andcapacity,are effective measures of the relative improvements for specific systems.consideringalso deployment aspects andthat different systems typicallyhave different cost structure,technical measures,like spectral efficiency,are however,as discussed in e.g.[1],insufficient to compare different systems. Ideally both costs and revenues should be included in the analysis,as in [2],andavailabilityof spectrum,previous assets, andother strategic issues needto be taken into account.due to difficulties in,e.g.,predictingendusers willingness to pay,this is quite complex.a simpler initial step,for relative comparisons only,is to compare the system cost for equal potential revenues (to simplify;number of supportedusers)andthis is also the focus of this paper. Klas Johansson W ireless@ KTH, The Royal Institute of Technology Electrum 418,S-16440Kista,Sweden klasj@ radio.kth.se More specifically,the radio access network infrastructure cost is normalized per user and compared between different single-andmulti-access system concepts as a function of traffic intensityper user and relative user coverage.the system concepts compared include the cellular systems W CDMA DCH,W CDMA HSDPA,and preliminary Long-Term 3G Evolution and4g proposals,as well as the W LAN system concepts IEEE b,a andg.also multi-access combinations of these,an expectedcharacteristic of future wireless networks [3],are included.to evaluate the benefits of the multi-access networks,a heterogeneous traffic densitymodel is applied. Recently a number of wireless network infrastructure cost analyses for single-access networks have been presented,e.g. [1]and[2].These conclude that the infrastructure cost,includingbothcapital expenditures (CAPEX)andoperational expenditures (OPEX),is largelyproportional to the number of access points deployed.equivalent cost figures per access point,are also presented,which can be used to simply assess the total infrastructure cost for a given deployment.models of the spatial distribution of mobile users have been presentedin e.g.[4] and[5].this paper combines the above results andextends the scope to cover multi-access networks. In what follows,section II brieflydiscusses the impact of the spatial traffic distribution on the cost efficiencyof different single-andmulti-access system concepts,andpresents the deployment principles usedin this study.an overview of the radio network,user behavior,and economical models and assumptions is given in Section III.Numerical results are presentedin Section IV,followedbyconclusions in Section V. II. DEPLOYMENT ASPECTS In scenarios with homogeneous,uniform traffic densities, single-access solutions withonlyone type of access point manage to maximize cost efficiency.for example,witha higharea traffic demandmicro cellular base stations maybe most cost efficient,whereas macro base stations typicallyyieldthe lowest cost in areas withless traffic per area unit.solutions witha mix of macro,micro andpico cells,as well as multi-access concepts,maybe expected to be more cost efficient than single access networks only in scenarios with heterogeneous traffic densities.yet,this is not a sufficient condition.it is also required that traffic peaks (hotspots)are veryfew and strong.

106 90 Chapter 6. Heterogeneous Infrastructure Cost (Paper 2) Traffic Density 1) Varying but low (average) traffic density 2) Varying and high Average Macro cell area capacity Traffic Density Traffic Density 3) Strongly varying, high, and correlated Micro cell area capacity Figure 1. Simple example of traffic density variations over space, and deployment of macro (light grey) and micro (darkgrey) access points. This is explained by the following example based on macro and micro cellular base stations. Assume that a single-carrier macro cell layer is deployed for fundamental coverage. As depicted in the first example in Figure 1, the capacity of this networkis sufficient to serve traffic density patterns for which the average is within the macro cell area capacity. Local areas with traffic demands exceeding the area capacity are supported. Then, if the average traffic density exceeds the total area capacity of the first macro carrier, either a second macro carrier, additional macro base stations or micro cells can be deployed. W hat solution that brings the lowest cost depends on the statistical distribution of traffic. In the second example, traffic is high and strongly varying with many relatively small peaks. Then adding a carrier to the macro-cellular layer, or deploying macro base stations more densely, is typically most efficient. Deploying a micro cell in each of the many local traffic density peaks would require a large amount of micro sites, and hence be costly, since each micro base station would have excess capacity and be poorly utilized. In situations like the one in example three, however, micro cells are motivated. Here, the peaks in traffic density are very strong and rather few, so that only a few micro cells need to be deployed, and the cost for this is lower than that of extending the macro layer. A heterogeneous traffic density alone is thus not sufficient for motivating micro and pico cell, or multi-access solutions from an infrastructure cost perspective. There are also requirements on (i) a high overall traffic density, (ii) strong variations in traffic density, and (iii) special spatial correlation properties. A. Deployment Principle The deployment principle used in this study is to first deploy macro cells for full area coverage, and then complement with micro or pico cells, or W LAN access points, where it is needed for capacity reasons. In more detail, first, the system area A sys is divided into N different 40x40m elements. The (center) position and the traffic generated in element n are denoted P n and TE n respectively. For each RAT r, candidate access point sites are positioned on a regular hexagonal grid, with site-to-site distances according to the AP cell radii. The position of AP m is denoted S m. Based on the site positions, an association between sites and traffic elements is made, so that the elements in the set S r m belong to AP m. The association is done so that traffic elements are associated with the closest site: { n min{ d( AP, P } m} r Sm = arg k k n ) = (1) where d(ap k,p n ) is the distance between AP k and element n. The offered traffic per AP is calculated as: r T m = TEn (2) r n S m In each site m, N r m cells (transceivers) are then deployed to fulfill the offered traffic, while not exceeding the maximum number of cells per AP, denoted N APmax : r r T N = m m min, NAPmaxm C, (3) r where C r is the capacity per access point of RAT r. Note that if T r m = 0, no access point is deployed. If T r m > C r, all the offered traffic cannot be handled by RAT r. In that case elements are allocated in an increasing order of offered traffic TE n until the maximum capacity per AP C r N APmax is reached. Remaining traffic that has to be served with other RATs (with a smaller cell radius and higher area capacity) will then belong to elements with the highest traffic TE n. More formally, the traffic elements S r m are sorted in order of offered traffic and indexed n. The number of elements M r max,m that are served by AP m is then determined by: M r Mmax,m = max M : TEn ' CrNAPmax,m (4) n' = 1 Then, the offered traffic in elements n =1.. M r max m is set to zero to determine the offered traffic for the next RAT to be deployed, i.e., r 0 n' M maxm TEn ' = (5) TE r n' n' > M maxm Once the above deployment is completed, which results in 100% coverage if no traffic remains after the last RAT is deployed, the cost for covering smaller fractions of users is calculated. This is done through sorting the access points in order of supported traffic divided by the access point cost. Given the final deployment for full coverage, deployment in this order represents the most cost efficient way to support a given traffic. It should be noted that no attempts have been made to optimize the deployment principle. Instead, the target has been a simple principle that is reasonably good and fair between system concepts. It could e.g. be noted that limiting W LAN access point positions to a regular grid is probably not optimum, but neither is allowing only one cell radius for cellular macro, micro, and pico cells. This tradeoff is discussed further in [7].

107 91 Figure 2. A sample of a traffic density map and WCDMA macro and WLAN access point deployment. III. MODELS AND ASSUMPTIONS This section describes the user behavior, system, and radio network models used to evaluate the different system concepts. Macroscopic models are used to enable a conceptual comparison between the concepts for different traffic densities. A. Trafic Density Models In order to capture the effects discussed in Section II, a heterogeneous user behavior is assumed. In short, based on the measurements and model proposed in [4] and statistics from [6], it is assumed that the user density is log-normaly distributed around a large-scale mean. The smal scale standard deviation of this distribution is adjusted so that assumed peak values in user density are achieved with reasonable probability. To fit the cel-level user density standard deviation to the value 0.4 (log-scale) reported in [4], a spatial correlation is assumed between elements. Reference user densities are created by multiplying typical suburban (su) and city centre (cc) population densities, 500 and inhabitants/km 2 respectively, with an assumed service penetration of 90% and an operator market share of 30%. Users are further characterized by an average busy hour traffic intensity, measured in data generation per unit time. As a basis for this, the traffic intensity of a private voice user during busy hour is used. This is assumed to be 20mErlang x 10kbps = 0.2kbps. Multiplying this with a factor N then forms traffic intensity reference values. As a reference, assuming that 0.6% of the monthly traffic is generated during each busy hour (typical for voice), a 1GB/month user corresponds to 13kbps, or N = 66. Traffic density maps (10x10km) are created by multiplying the user densities and per-user traffic intensities. The gray scale contour in Figure 2 depicts a realization of traffic density generated by the model. Note that no explicit service is assumed. The evaluation is applicable to al services for which the system models, i.e. access point capacity and coverage, are valid, and that are within the capabilities of the access technology. These capabilities differ significantly between some of the access technologies. For example, 4G concepts should be compared with WCDMA only for services supported by both networks. TABLE I. ACCESS POINT CHARACTERISTICS. Radius Capacity Cost Coeff. W CDMA DCH macro 1000m [3-9] x 1Mbps 1 (55/45%) W CDMA DCH micro 250m [1-2] x 1Mbps 0.45 (45/55%) W CDMA DCH pico 100m 1Mbps 0.3 (35/65%) W CDMA HS macro 1000m [3-9] x 2.5Mbps 1 (55/45%) W CDMA HS micro 250m [1-2] x 2.5Mbps 0.45 (45/55%) W CDMA HS pico 100m 2.5Mbps 0.3 (35/65%) S3G macro 1000m 3 x 15Mbps 1 (55/45%) S3G micro 250m 15Mbps 0.45 (45/55%) S3G pico 100m 15Mbps 0.3 (35/65%) S3G macro m 3 x 15Mbps 1 (55/45%) 4G 700m 3 x 100Mbps 1 (55/45%) 4G micro 175m 100Mbps 0.45 (45/55%) 4G pico 70m 1Gbps 0.3 (35/65%) 4G relay 1850m 100Mbps 6.4 (65/35%) IEEE b 40m 6Mbps 0.13 (3/97%) IEEE g 40m 22Mbps 0.13 (3/97%) IEEE a 20m 22Mbps 0.13 (3/97%) IEEE n 20m 100Mbps 0.13 (3/97%) B. System and Radio Network Models Access points of different access technologies are characterized with different maximum cel radi and capacities; see Table I (herein a cel is defined as a combination of a sector and carrier frequency). Al figures are for the downlink and roughly valid for an urban environment without strict requirements for indoor coverage. However, with the simplified modeling used, without explicit radio network models, the models are applicable for arbitrary environment, deployment and service scenarios for which the system models are valid. The WCDMA DCH and HS-DSCH figures, assuming a 15MHz spectrum alocation, are taken as reference values, and Long-Term 3G Evolved [8] (henceforth shortly denoted S3G ) and 4G figures are derived from these. For S3G, a 20MHz spectrum alocation is assumed. Together with a spectrum efficiency assumption of 0.75bps/Hz/cel, this results in a capacity per cel of 15Mbps. The same power density as for WCDMA is also assumed, resulting in the same cel radius. To investigate the impact of coverage, a hypothetical S3G system operating in 450MHz spectrum, is also studied. Its cel radius is simply based on frequency difference and a path-loss exponent of 3.5. For 4G, a 100MHz spectrum is assumed, together with a slightly improved spectrum efficiency of 1bps/Hz/cel. This results in a capacity per cel of 100Mbps. A four times lower power density is assumed for the wider 4G carrier than for WCDMA. Assuming a distance atenuation exponent of 3.5, this results in a 30% reduced cel radius. Micro and pico cel capacities are assumed equal to the macro-cel capacities (per cel). The WLAN figures assume single-cel, non-interfered access points. In coordinated multi-cel scenarios these figures decrease some 20-40% for b and g. In noncoordinated multi-operator scenarios, the capacity is shared equaly between the operators. A simple 2-hop regenerative relaying concept is also evaluated. It is assumed that the access point is surrounded by a ring of six relay nodes, each with the same cel radius as a regular macro cel access point. This results in an equivalent cel radius of 7 of the original cel radius. The capacity is limited by the access point, and assumed to remain at 100Mbps despite the potentialy favorable channel conditions towards the relay

108 92 Chapter 6. Heterogeneous Infrastructure Cost (Paper 2) In fra s tru c tu re C o s t p e r M o n th a n d G b y te [ ] /m o n th s u 1 F ra c tio n o f S u p p o rte d U s e rs 9 0% T ra ffic D e n s ity [M b p s /k m 2] Figure 3. Infrastructure cost per 1GB/month user versus traffic density for 90% supported users. nodes. Note that more sophisticated relaying concepts than that evaluated here exist, with potential to further improve coverage and capacity. The access points are also characterized with the cost coefficients given in Table I. These estimate the total infrastructure cost associated with one access point, including CAPEX for radio access networkequipment and site build out, as well as OPEX for site rental, transmission, power consumption and O&M over a 10-year period, assuming a 10% discount rate. The figures build on those used in [1], where in turn equipment cost estimates were provided by the Gartner Group and other cost estimates were based on [2]. In this study minor updates for radio networkcontrollers, power consumption and O&M, and addition ofw LAN, also based on [2], have been made. The coefficients in Table Iare normalized to an estimated value for a cellular macro base station, assumed to be 300k (slightly higher than in [1]due to the above modifications). The components ofthe cost coefficients are further discussed in [1] and [2], Table Imerely includes the fractional CAPEX and OPEX, which in turn are dominated by site and transmission costs respectively. The total infrastructure cost for green-field operators can be calculated as the number ofaccess points of each type multiplied with the corresponding cost coefficients. Note that this model excludes costs for core networknodes as well as costs for spectrum (due to differences in regulation, a generally applicable spectrum cost model is very difficult to define). This makes the costs incremental, i.e. measuring the additional cost for covering a new area, once the core network and spectrum is paid for. Terminal costs, as well administrative costs, e.g. for marketing and billing, are also excluded. IV. s u 10 c c 1 s u 100 NUMERICAL RESULTS In this chapter numerical results are presented on the form infrastructure cost per transferred data unit (1GB)and month versus traffic density and fractional coverage. In Figure 390% ofthe traffic (users)are supported and in Figure 4only a small fraction, 20%, ofthe users are served. Some reference levels are marked on the traffic density axis. These are combinations ofsuburban (su)or city center (cc)environments as defined above, and traffic intensities per user measured in N times voice (0.2kbps). On the cost axis, a reference level of 30per c c 10 s u g W C D M A D C H W C D M A H S D C H & 11g H S & 11g S 3G S 3G G 4G re la y c c 100 c c 1000 In fra s tru c tu re C o s t p e r M o n th a n d G b y te [ ] /m o n th s u 1 F ra c tio n o f S u p p o rte d U s e rs 20% s u 10 c c 1 s u T ra ffic D e n s ity [M b p s /k m 2] Figure 4. Infrastructure cost per 1GB/month user versus traffic density for 20% supported users. GB and month is marked. This is a rough estimate ofwhat a typical user is willing to spend on mobile communications today. Generally, for all system concepts, the infrastructure cost per GB decreases with traffic density while the systems are coverage limited, and flattens when the system becomes capacity limited. A. Commercialy Available Systems Beginning with the g W LAN and 90% ofthe traffic served, it is seen that for low traffic densities the cost per GB is very high. In a suburban environment with a voice-like traffic intensity per user (su1), the infrastructure cost reaches /GB/month. To get down to a reasonable cost per GB ( 30), a traffic density of10 Mbps/km 2 is required, approximately corresponding to su500or cc10scenarios. For a fraction ofsupported users ofonly 20%, as shown in Figure 4, the cost per user for g decreases significantly (as expected). The reasonable cost of 30/GB/month is now reached at 1Mbps/km 2 instead, or roughly a cc1scenario. This indicates the degree ofcoverage that can be expected to be profitable for W LAN only operators. W CDMA DCH and HSDPA yield about 50times lower cost for moderate traffic densities. These systems reach 30per GB and month already at 0.2Mbps/km 2 corresponding to su10scenarios. W CDMA HSDPA becomes capacity limited at higher traffic densities than W CDMA DCH, and therefore yields lower costs at high traffic densities. The crossover point between W CDMA HSDPA and g is about 100Mbps/km 2, or cc100. W ith 20% ofthe users covered W CDMA HSDPA is more expensive than W LAN at 30Mbps/km 2 (su1000/cc30), whereas with 90% coverage W LAN only systems gives a lower cost at first around 100Mbps/km 2 (cc100). The multi-access concepts, W CDMA DCH or W CDMA HSDPA combined with g, are seen to yield the lowest cost ofthe included subsystems. However, the gain as compared to, e.g., a single access W CDMA HSDPA system (with hierarchical cell structures)is evident only at very high traffic (> 300Mbps/km 2 ). W ith the models and assumptions used, there is hence no significant multi-access cost reduction. On the other hand there is neither any loss, and there is thus no c c 10 s u g W C D M A D C H W C D M A H S D C H & 11g H S & 11g S 3G S 3G G 4G re la y c c 100 c c 1000

109 93 cost drawback for a mobile network operator deploying macro and micro cells first,and then adding W LAN only in hotspots, as compared to a pure W LAN operator. It may also be noted that HSDPA alone is a better solution than both W CDMA DCH and g for traffic loads up to 100Mbps/km 2. B. Future Concepts The S3G concept,withsimilar coverage and cost characteristics as W CDMA DCH and HSDPA,also yield the same cost as these concepts at low and moderate traffic densities (while the systems are coverage limited). S3G however remains coverage limited for higher traffic densities,and yields lower costs for traffic densities exceeding 2Mbps/km 2. S3G is also seen to be a better alternative than W CDMA HSDPA combined with g for the full range of studied traffic densities. The benefit of large coverage is also seen from the hypothetical S3G 450system. Due to its large cell radius,it yields cost almost 10times lower cost than the other cellular concepts for traffic densities up to around 2Mbps/km 2. Even for high traffic densities it is better than standard S3G despite the same capacity per AP. This indicates that despite a highmean traffic, there are large areas withless traffic where a large coverage per AP is important. The preliminary 4G concept,without relaying,is seen to suffer somewhat from its reduced coverage for traffic loads up to about 10Mbps/km 2. Beyond this level it yields the lowest cost per user. The 4G concept withrelaying provides slightly lower cost than 4G without relaying for moderate traffic densities,but still higher cost than bothw CDMA and S3G. C. Pricing Strategy and Service Ofering Consequences The cost per data unit can be mapped to a cost per user (and service)in several ways. This is a quite complexarea,which has been subject to many studies. Value based pricing is,however,nowadays most often used in practice and there is typically little relation between price and production cost [9]. Yet, the (incremental)production cost will tell if a service would be profitable or not given the end user pricing possibilities.. Assuming a traffic independent cost the average cost per user is given by the total infrastructure cost divided withthe number of supported users. This can also be calculated by multiplying the cost per unit data withthe average per user traffic intensity. Alternatively,assuming a linearly traffic dependent cost,the cost per individual user is given by multiplying the cost per unit data withthe individual user traffic intensity. Several alternatives in between these extremes of course exist. The results presented here are valid for all these alternatives. An interesting observation is that,withthe models and assumptions used,the incremental infrastructure cost for 1GB per monthper user can be kept below 30for traffic densities exceeding about 0.2Mbps/km 2. Adding margins for excluded costs (marketing,customer care,core network and service platforms,profit,taxes,etc.)about 1Mbps/km 2 is probably a more realistic value. This roughly corresponds to a city center area withtoday s voice traffic,or a suburban area with40times this traffic per user. V. CONCLUSIONS The results of this study indicate that,withthe models and assumptions used,single-access solutions with high capacity macro cells yield the lowest costs per user,despite a spatially non-uniform traffic demand. Examples of such systems are W CDMA HSDPA and the preliminary S3G concept. Operator deployed W LAN-only solutions yield high costs even if the requirement on fractions of supported users is small (20%). Except for at very hightraffic,a multi-access network composed of W CDMA DCH or HSDPA macro and micro cells combined withieee g access points do not yield lower costs per user than using a single-access W CDMA DCH or HSDPA network consisting of macro,micro and pico cells. This is because several W LAN access points are required in each hotspot due to the poor coverage,which results in an excess capacity and poor utilization of eachap. A hotspot concept withbetter coverage could thus lead to better results also for moderate average traffic densities. For mobile network operators having a 3G license introducing W LAN hotspots hence need to be motivated by other factors;suchas access technology capabilities and spectrum. Among the 4G concepts,it is seen that a simple relaying solution withmacro-like relays nodes may yield improved cost efficiency in areas withmoderate traffic demand,up to some 10s of Mbps/km 2. For traffic densities beyond this level,this particular relaying solution is not motivated. The overall lowest cost is enabled by a hypothetical high-capacity cellular system operating in 450MHzspectrum. This indicates the importance of good coverage,whichis of course valid also for alternative means to achieve it. Future studies could make use of more refined system and economical models. In particular empirical data on traffic demand for mobile data services would be useful to improve the heterogeneous traffic density model. REFERENCES [1] K. Johansson,et al., Relation between base station characteristics and cost structure in cellular networks,in the Proc. of IEEE Personal, Indoor and Mobile Communications (PIMRC),2004. [2] F. Loizillon et al., Final results on seamless mobile IP service provision economics,ist TONIC Deliverable no. 11,Oct [3] N. Niebert et al., Ambient Networks: An Architecture for Communication Networks Beyond 3G, in IEEE Wireless Communications,Vol. 11,Issue. 2,April 2004,pp [4] U. Gotzner et al., Spatial Traffic Distribution in Cellular Networks,in Proceedings of IEEE Vehicular Technology Conference,1998. [5] R. Ganeshand K. Joseph, Effect of non-uniform traffic distributions on performance of a cellular CDMA system, Universal Personal Communications Record,October [6] US Census Bureau,Table GCT-PH1. Population,Housing Units,Area, and Density :2000,available at [7] K. Johansson and A. Furuskär, Cost efficient capacity expansion strategies using multi-access networks, in Proceedings of IEEE Vehicular Technology Conferencespring,2005. [8] Third Generation Partenership Project (3GPP),RP , Proposed Study Item on Evolved UTRA and UTRAN,available at pp.org/ftp/tsg_ran/tsg_ran/tsgr_26/docs/pdf/rp pdf. [9] T. T. Nagle and R. K. Holden,"The Strategy and Tactics of Pricing", Second Edition,Prentice Hall,1997.

110

111 Chapter 7 Cost Efficient Capacity Expansion Strategies using Multi-Access Networks (Paper 3) Klas Johansson and Anders Furuskär, In Proc. IEEE VTC2005 Spring, May

112

113 97 Cost efficient capacity expansion strategies using m ulti-access netw ork s K las J oh ansson W ireless@ K T H, R oyal Institute of T ech nology E lectrum 4 1 8, S E K ista, S w ed en E m ail: k lasj@ rad io.k th.se A nd ers F urusk är W ireless A ccess N etw ork s, E ricsson R esearch K ista, S w ed en E m ail: and ers.furusk ar@ ericsson.com Abstract Multi-access networks and hierarchical cell structures are two com m on cap acity ex p ansion strateg ies for m ob ile network op erators. In b oth cases costs can b e m inim iz ed for a set of av ailab le radio access technolog ies, g iv en heterog eneous req uirem ents on area cov erag e, cap acity and q uality of serv ice. I n this p ap er we q uantify the infrastructure cost for a m ulti-access network com p osed of m acro cellular H S D P A b ase stations and I E E E g W L A N access p oints. T he network is dim ensioned for an urb an env ironm ent using a stochastic m odel for heterog eneous traffi c density. W ith the used assum p tions and m odelling it is shown that a com b ination of H S D P A b ase stations dep loy ed with m cell radius tog ether with W L A N in hot sp ots are suffi cient for av erag e traffi c densities up to around 5 0 Mb p s/km 2 (5 0 tim es the traffi c of ty p ical p riv ate v oice users today ). I n order to ev aluate the sensitiv ity to different desig n features, we introduce the elasticity of infrastructure cost and can thereb y show that it is m ore im p ortant to im p rov e cap acity in H S D P A than cov erag e p er g access p oint. H owev er, with a sp arse dep loy m ent of H S D P A m acro cells (8 0 0 m radius) infrastructure cost is m ore elastic to g cov erag e. T he p ap er also indicates som e p ossib ilities to differentiate future radio access technolog ies towards current sy stem s. low cost, sh ort range access points suitab le for h ot spots. M ore specifically, tw o prob lem s w ill b e ad d ressed ; (i) th e trad eoff b etw een th e num b er of m acro cellular B S s and com plem entary W L A N A P s need ed, and (ii) w h ich param eters th at are m ost im portant to im prov e in each system in ord er to furth er red uce costs. T h is stud y com plem ents th e results presented in [3 ], w h ich ev aluates th e cost w ith single and m ulti-access d eploym ent for a num b er of, b oth com m ercially av ailab le, and future rad io access tech nologies. In b oth stud ies th e scope is lim ited to th e rad io access netw ork. S pectrum license fees and oth er costs th at are com m on for th e w h ole m ob ile netw ork are th us exclud ed. L ik ew ise are a num b er of oth er param eters th at also are of im portance for a m ob ile operator s d eploym ent strategy; e.g. topology, prev ious assets, and (perh aps forem ost) d em and and regulatory req uirem ents. Y et, th e ob jectiv e is to contrib ute to a b etter und erstand ing of th e role of m ulti-access as capacity expansion strategy and for th is reason a stoch astic (log-norm al) spatial d istrib ution of traffic is assum ed [3 ]. S erv ice allocation principles for m ulti-access netw ork s h av e prev iously b een treated in, e.g., [4 ] and [7 ]. T h ese stud ies, I. I N T R O D U CT I O N h ow ev er, ad d resses th e prob lem of selecting rad io access M ulti-access netw ork s are prom ising d ue to h igh ly v arying netw ork for users th at are cov ered b y m ultiple system s, and not req uirem ents ov er tim e and geograph ically on m ob ility, q uality d im ensioning of each sub system. In ad d ition, E U h as recently of serv ice, capacity, etc., and th e inh erent trad eoff in all initiated th e A m b ient N etw ork s project w h ich d eals w ith a w ireless system s b etw een range and feasib le d ata rates. H ence, b y d eploying a h eterogeneous w ireless netw ork, w ith m ultiple stand ard s and /or h ierarch ical cell structures, an operator can ad apt capacity to d em and and th ereb y low er th eir capital and num b er of aspects, m ainly tech nically b ut also b usiness w ise, of h eterogeneous netw ork s [9 ] and th ere are h ence a num b er of ongoing stud ies in th is area. P rev ious stud ies consid ering th e cost structure of m ob ile system s includ e, e.g., [6 ], [8 ], [1 1 ], operational expend itures (CA P E X /O P E X ). T h ere are in fact and [1 2 ]. F rom th ese stud ies it is clear th at th e cost structure m any w ireless stand ard s in th e m ark et alread y tod ay, and ev en m ore are und er d ev elopm ent. S om e system s, lik e 3 G, of m ob ile netw ork s tod ay is d om inated b y th e rad io access netw ork. M oreov er, th e stud ies in [6 ] and [8 ] prov id e a b asis are d esigned to b enefit from econom y of scope, m eaning th at for th e tech no-econom ical m od elling used in th e seq uel of th is cost efficiency is ach iev ed since a w id e range of serv ices can paper w h ich is outlined as follow s. b e prov id ed w ith th e sam e system ov er w id e areas, potentially S ection II cov ers b asic m od elling and assum ptions related to serv ing m any users. O th er stand ard s are stream lined for B S perform ance and costs, as w ell as netw ork d im ensioning. specific serv ices, such as 2 G for w id e-area m ob ile v oice and Infrastructure cost estim ations for a m ulti-access netw ork is W L A N for local h igh -speed d ata connectiv ity. T h ere are h ence reasons to b eliev e th at m ulti-access netw ork s is a sustainab le d eploym ent strategy for m ob ile netw ork operators. presented in S ection III for incum b ent and greenfield operators respectiv ely, togeth er w ith an analysis of th e elasticity of cost w ith respect to th e capacity, cov erage and cost per B S. In T h is paper treats cost efficient d eploym ent strategies for S ection IV w e d iscuss h ow th e stud ied system s could b e netw ork s com posed of H S D P A m acro cellular b ase stations im prov ed tech nically and econom ically, and point at a few (B S ) and IE E E g access points (A P ). T h ese sh ould gaps th at potentially could b e filled b y future rad io access represent system s w ith long range for w id e area cov erage and tech nologies. T h e paper is conclud ed in S ection V.

114 98 Chapter 7. Cost Efficient Capacity Expansion (Paper 3) II. SY ST E M MO D E L S AN D PE R F O R MAN C E ME ASU R E S A m a c ro s c o p ic m o d e l is u s e d to c a p tu re k e y te c h n ic a l a n d e c o n o m ic a l p a ra m e te rs th a t in fl u e n c e th e in fra s tru c tu re c o s t fo r a ty p ic a l (w e s te rn E u ro p e a n ) m o b ile n e tw o rk o p e ra to r. T h is is b a s e d o n p re v io u s w o rk p re s e n te d in [4 ] a n d fa c to rs n o rm a lly m o d e lle d in n e tw o rk d im e n s io n in g a n d c a p a c ity a n a ly s is ; lik e in te rfe re n c e, p ro p a g a tio n, e tc., a re e x o g e n o u s to th e m o d e l. A. Network dimensioning and traffic modelling In a m u lti-a c c e s s n e tw o rk APs w ith s h o rte r ra n g e a n d c o s t m a y b e u s e d in h o t s p o ts, w h e re a s m a c ro B Ss w ith a h ig h a re a c o v e ra g e a re u s e d to p ro v id e b a s ic c o v e ra g e a n d c a p a c ity. In p ra c tic e ra d io n e tw o rk p la n n in g a n d d im e n s io n in g is a n ite ra tiv e p ro c e s s [2 ] a n d a s th e n e tw o rk m a tu re s it is g ra d u a lly a d a p te d to lo c a l d e m a n d. T o m o d e l th e d e p lo y m e n t s tra te g y w e u s e a h e u ris tic a lly b a s e d m e th o d w h ic h is d e s c rib e d in m o re d e ta il in [3 ]. T h e b a s ic id e a is, in o u r e x a m p le, to fi rs t d e p lo y H SD PA s ite s w ith a g iv e n c e ll ra d iu s a n d lo a d th e m w ith a s m a n y tra n s c e iv e r u n its a s n e e d e d (u p to th e m a x im u m c a p a c ity p e r B S). If th is is n o t s u ffi c ie n t to s e rv e th e tra ffi c d e m a n d, g APs a re d e p lo y e d in a re a s w ith h ig h e s t tra ffi c d e n s ity. Id e a lly, e a c h s u b s y s te m s h o u ld b e ju s t c a p a c ity lim ite d fo r a c o s t e ffe c tiv e d e p lo y m e n t to a v o id e x c e s s c a p a c ity p e r AP. T ra ffi c d e n s ity is m o d e lle d a s in [3 ] w ith a lo g -n o rm a l, s p a tia lly c o rre la te d, s to c h a s tic v a ria b le o v e r th e s e rv ic e a re a (1 0 x 1 0 k m ) w h ic h h e re in is d iv id e d in to s a m p le s o f 2 0 x 2 0 m. A s ta n d a rd d e v ia tio n o f 7 d B h a s b e e n a s s u m e d a n d th e c o rre - la tio n d is ta n c e is m, w h ic h m a tc h e s th e c e ll le v e l s ta tis tic s p re s e n te d in [5 ]. In a ll n u m e ric a l e x a m p le s th e a v e ra g e p o p - u la tio n d e n s ity is in h a b ita n ts /k m 2, c o rre s p o n d in g to a c ity c e n te r e n v iro n m e n t. T h e o p e ra to r u n d e r s tu d y is a s s u m e d to h a v e a 3 0 % m a rk e t s h a re a n d th e s e rv ic e p e n e tra tio n is 9 0 %. H e n c e, th e n u m b e r o f s u b s c rib e rs is in a v e ra g e u s e rs /k m 2 (lo c a lly th is is m u c h h ig h e r). B. P ath loss models Alth o u g h n o t u s e d e x p lic itly fo r th e n e tw o rk d im e n s io n in g, tw o s ta n d a rd p a th lo s s m o d e ls w ill b e u s e d to e s tim a te th e c e ll ra n g e o f m a c ro c e lls in Se c tio n IV ; th e C O ST H a ta (v a lid fo r 1 k m < d < 2 0 k m ) a n d C O ST W a lfi s c h -Ik e g a m i (v a lid fo r 2 0 m < d < 5 k m ). In g e n e ra l th e p a th lo s s in s m a ll c e lls, a n d in p a rtic u la r in d o o rs, is c a s e s p e c ifi c a n d n o t re a d ily d e s c rib e d w ith a s ta tis tic a l m o d e l. H o w e v e r, th e s e c o m m o n ly u s e d m o d e ls p ro v id e d in [1 ] s h o u ld g iv e a n in d ic a tio n o f fe a s ib le c e ll ra n g e s a n d th e s a m e m e th o d o lo g y h a s b e e n u s e d in, e.g., [2 ] fo r ru d im e n ta ry c o v e ra g e a n a ly s is. C O S T W alfisch -Ikegami - a s s u m in g b u ild in g s e p a ra tio n (3 0 m ), s tre e t w id th (1 5 m ), b u ild in g h e ig h t (2 5 m ), a n d a 9 0 d e g re e a n g le o f a rriv a l fo r b u ild in g re fl e c tio n s : L b = ( fc ) lo g 1 0 f c lo g 1 0 d. (1 ) C O S T H ata - w ith 3 d B m e tro p o lita n a re a c o rre c tio n fa c to r a n d a n O k a m u ra -H a ta B fa c to r a s re c o m m e n d e d fo r la rg e c itie s : L b = lo g 1 0 f c lo g 1 0 d. (2 ) T AB L E I AC C E SS PO IN T C H AR AC T E R IST IC S HSDPA g R a d iu s m 4 0 m C a p a c ity [3-9 ] x 2.5 Mb p s 2 2 Mb p s C o s t c o e ffi c ie n t 1 (5 5 % /4 5 % ) (3 % /9 7 % ) (C APE X /O PE X ) p e r c e ll In b o th m o d e ls th e B S h e ig h t w a s 3 0 m a n d m o b ile s ta tio n h e ig h t 1.5 m. L b d e n o te p a th lo s s in d B, f c is th e c a rrie r fre q u e n c y in MH z, a n d d is th e d is ta n c e b e tw e e n B S a n d m o b ile s ta tio n g iv e n in k m. C. Access p oint p erformance and cost assu mp tions APs a re c h a ra c te riz e d w ith d iffe re n t c e ll ra d ii, c a p a c itie s a n d c o s ts ; s e e T a b le I. C a p a c ity c o e ffi c ie n ts fo r g a s s u m e s n o c o -c h a n n e l in te rfe re n c e w h e re a s H SD PA d o e s, d u e to th e c e llu la r d e p lo y m e n t a n d lim ite d fre q u e n c y s p e c tru m w e a s s u m e 3 c a rrie rs x 5 Mh z (1 5 MH z in to ta l) fo r d o w n lin k. N o tic e th a t th e m a x im u m c a p a c ity fo r H SD PA a n d g is s im ila r, a n d 2 2 Mb p s, s o th e AP w ith lo w e s t c o s t p e r tra n s m itte d b it is e s s e n tia lly d e te rm in e d b y th e g e o g ra p h ic a l d is trib u tio n o f tra ffi c. C o s t c o e ffi c ie n ts in c lu d e b o th C APE X a n d O PE X a n d a re h e n c e fo rth d e n o te d AP c o s t. F o r H SD PA w e u s e th e c o s t fo r a m a c ro B S d e riv e d in [6 ], w h ic h in tu rn w a s b a s e d o n e s tim a te s p ro v id e d b y th e G a rtn e r G ro u p a n d [8 ]. In th e n u m e ric a l e x a m p le s w e h a v e a s s u m e d th a t a m a c ro B S c o s ts e3 0 0 k. C o s ts fo r ra d io n e tw o rk c o n tro lle rs (R N C ) a n d e le c tric a l p o w e r h a v e b e e n a d d e d a s c o m p a re d to th e e s tim a te s in [6 ]. T a b le I a ls o s u m m a riz e s th e c o s t s tru c tu re in te rm s o f C APE X a n d O PE X a n d th e a d d itio n a l c o s t fo r e x tra c e lls (d e fi n e d a s a c a rrie r fre q u e n c y a n d s e c to r) in H SD PA. An in c u m b e n t o p e ra to r th a t a lre a d y h a s s ite s fo r le g a c y s y s te m s in s ta lle d m a y re u s e m o s t o f th e s e s ite s a n d w e a s s u m e th a t th is lo w e rs th e c o s t fo r H SD PA B Ss w ith 2 5 %. F o r g n e w e s tim a te s h a v e b e e n d e d u c te d b a s e d o n [8 ]. O PE X is c a lc u la te d in p re s e n t v a lu e o v e r a 1 0 -y e a r p e rio d, u s in g a 1 0 % d is c o u n t ra te (s e e fu rth e r [6 ]). F o r th e s a k e o f s im p lic ity th e n e tw o rk is d im e n s io n e d to c a rry th e s a m e tra ffi c d u rin g th e w h o le n e tw o rk life s p a n. D. Infrastru ctu re cost measu res T h e b a s ic m e a s u re fo r c o s t e ffi c ie n c y u s e d is th e infrastru c- tu re cost p er G B and month. In d o in g th is w e a s s u m e th a t 0.6 % o f th e m o n th ly tra ffi c is c a rrie d d u rin g e a c h b u s y h o u r, w h ic h ro u g h ly c o rre s p o n d s to th e tra ffi c p a tte rn in c u rre n t c e llu la r s y s te m s, a n d th a t th e n e tw o rk is d im e n s io n e d a c c o rd in g to a v e ra g e a g g re g a te th ro u g h p u t (p e r a re a s a m p le ). H e n c e, th e re s u lts a n d c o n c lu s io n s s h o u ld h o ld fo r a ll tra ffi c m ix e s th a t fa ll w ith in th e p e rfo rm a n c e p a ra m e te rs g iv e n in T a b le I. As a se n sitiv ity a n a ly sis w e e stim a te th e elasticity of infrastru ctu re cost. E la s tic ity is c o m m o n ly u s e d in e c o n o m ic s to m e a s u re th e in c re m e n ta l p e rc e n ta g e c h a n g e in o n e v a ria b le w ith re s p e c t to a n in c re m e n ta l p e rc e n ta g e c h a n g e in a n o th e r v a ria b le [1 0 ]. W e d e fi n e th e e la s tic ity o f a p a ra m e te r X (w h ic h

115 99 In fra s tru c tu re C o s t p e r M o n th a n d G B [E u ro ] V o ic e 10 x v o ic e 100 x v o ic e A v e ra g e T ra ffic D e n s ity [M b p s /k m 2 ] 200m 4 00m 8 00m 1000m Fig. 1. Infrastructure cost per GB and month for an incumbent operator with a multi-access network consisting of H SD P A macro BSs and IE E E g A P s. T he curv es depict different cell radii in the macro cells. herein is either cost, cov erage or capacity per A P ) on the total infrastructure cost C as: E C,X = C/ C X / X. (3 ) T he macro cellular site density that minimiz es cost with single access deploy ment is not necessarily optimum with a multi-access deploy ment. It depends on the traffi c demand, and how that v aries ov er the total serv ice area (as discussed in [3 ]). If the macro BSs are deploy ed too sparsely, the remaining areas that hav e to be cov ered with, in this case, g A P s may be too large with a resulting ov ercapacity per access point. T his tradeoff is illustrated in Figure 1, where the infrastructure cost per GB and month is shown for an incumbent operator with different cell radius in the macro cell lay er and 9 0 % of T A BL E II SU M M A RY O F M O N T H L Y IN FRA ST RU C T U RE C O ST S P E R GB A N D T H E C O ST A D V A N T A GE FO R IN C U M BE N T S T O W A RD S GRE E N FIE L D O P E RA T O RS. Voice 1 0 x v oice 5 0 x v oice T r a ffi c d en s ity 1M bps/k m 2 10 M bps/k m M bps/k m 2 H S D P A r a d iu s m m m H S D P A B S d en s ity BSs/k m BSs/k m BSs/k m 2 H S D P A cells /B S 1.4 cells 5.7 cells 6.8 cells W L A N A P d en s ity 0 A P s/k m 2 2.1A P s/k m 2 19 A P s/k m 2 I n cu m b en t op er a tor e6.8 e2.9 e1.7 G reen fi eld op er a tor e8.8 e3.4 e1.9 C os t a d v a n ta g e 2 4 % 15 % 10 % for in cu m b en t the offered traffi c supported. A s ex pected the cost v aries with traffi c density and deploy ed macro cell radius. T he results are summariz ed in T able II for 1, 10 and 5 0 times the traffi c of ty pical priv ate v oice telephony users (2 0 me rl, 10 k bps, see [3 ]). A t 10 x v oice, in this ex ample eq ual to a traffi c density of 10 M bps/k m 2, an H SD P A cell radius of m y ields the lowest cost. In av erage six cells (two carriers in three sectors) are used per H SD P A BS and there are approx imately two g A P s per k m 2 deploy ed in hot spots. T he lowest cost for another tenfold traffi c increment is achiev ed with m cell radius. T he av erage number of cells per H SD P A BS is approx imately the same. H owev er, the number of W L A N A P s is now 19 /k m 2. N otice also that the incremental cost T hus, a negativ e E C,X corresponds to a decreased cost and per transmitted GB fl attens when the macro cellular network if E C,X is positiv e the infrastructure cost increases (independently becomes capacity limited which is due to poor cov erage in of if the changed v ariable X is increased or decreased). T hus, the higher absolute elasticity, the greater impact X has on C. N otice that elasticity q uite often is calculated in absolute g. Results for Greenfi eld operators hav e the same shape and the difference in infrastructure cost per GB and month is giv en v alue. A 5 0 % change in X has been used in all studied cases so T able II. T he cost adv antage of incumbents v anishes as traffi c that X / X = 0.5. A s an ex ample, assume that we want to increase and g has to be deploy ed to a greater ex tent. estimate the elasticity with respect to A P cov erage in g. T his highlights how important it is for incumbents to acq uire E C,X = 1 would then mean that the total infrastructure cost more spectrum to remain competitiv e if traffi c surges in the C decreases with 5 0 % if the cell area is doubled. I.e., if the A P range were long run. = 4 9 m instead of 4 0 m. T hese results also ex plain how operators could ex ploit W L A N in the short run instead of building a denser macro III. N U M E RIC A L RE SU L T S In this section the tradeoff between H SD P A cell radius (site network if traffi c suddenly increases. In particular, considering the relativ ely small sunk costs in W L A N, see T able I, this density ) and the number of g A P s will fi rst be q uantifi ed could be economically justifi ed during transition periods. through simulations using the models outlined abov e, which For ex ample, increasing capacity from 5 to 10 M bps/k m 2 are described more thoroughly in [3 ]. T hen the elasticity of with W L A N instead of deploy ing more macro sites increase infrastructure cost is deriv ed for a few base line confi gurations. costs with less than 10 0 % per GB calculated ov er 10 y ears A. Traffic density and HSDPA site distance (comparing the results for m and m H SD P A radii). In the long run increasing capacity in the macro cell lay er is, howev er, more cost effi cient as we will discuss further nex t. B. E lasticity o f infrastru ctu re co st T wo reference sy stems adapted for approx imately 10 and 5 0 M bps per k m 2 will be used as ex amples to analy z e what k ey parameters that would lower infrastructure costs the most. For these sy stems, with m and m cell radius respectiv ely, the elasticity of infrastructure cost E C,X is plotted Figure 2 with the following v ariables changed (one per curv e): decreased H SD P A cost coeffi ecient,

116 100 Chapter 7. Cost Efficient Capacity Expansion (Paper 3) E la s tic ity o f In fra s tru c tu re C o s t V o ic e 10 x v o ic e 100 x v o ic e A v e ra g e T ra ffic D e n s ity [M b p s /k m 2 ] (a) HSDPA cell radius 400m H S D P A c o s t H S D P A c a p a c ity g c o s t g c o v e ra g e E la s tic ity o f In fra s tru c tu re C o s t V o ic e 10 x v o ic e 100 x v o ic e A v e ra g e T ra ffic D e n s ity [M b p s /k m 2 ] (b ) HSDPA cell radius 8 00m H S D P A c o s t H S D P A c a p a c ity g c o s t g c o v e ra g e F ig. 2. E lasticity o f in frastructure co st fo r an HSDPA an d g multi-access n etw o rk w ith resp ect to differen t ch an g es in desig n p arameters. T h e referen ce sy stem is adap ted fo r ap p ro x imately 5 0 x v o ice traffi c (left g rap h ) an d 1 0 x v o ice traffi c (rig h t g rap h ). All b ut th e ch an g ed v ariab les are k ep t co n stan t. decreased g co st co effi ecien t, in creased HSDPA B S cap acity, an d in creased g AP co v erag e. E lasticity o f in frastructure co st w ith resp ect to th e co st co - effi cien ts sh o w h o w larg e sh are o f th e in frastructure co st th at stems fro m each sy stem. W ith 400m cell radius each sub sy stem stan ds fo r 5 0% o f th e co st at 9 0M b p s/k m 2, w h ile th e cro sso v er o ccurs at aro un d 1 5 M b p s/k m 2 w ith 8 00m cell radius. At 1 M b p s/k m 2 o n ly HSDPA b ase statio n s are dep lo y ed an d th ere is h en ce o v er-cap acity in th e macro cell lay er. W e can also see th at HSDPA cap acity is mo re imp o rtan t to imp ro v e th an g co v erag e at all studied traffi c den sities w ith 400m HSDPA cell radius. W ith mo re sp arsely dep lo y ed HSDPA sites, h o w ev er, in frastructure co st is mo re elastic to g AP co v erag e th an HSDPA B S cap acity fo r traffi c den sities ab o v e 5 0M b p s/k m 2. Alth o ug h n o t in cluded in th e g rap h s, simulatio n s also sh o w th at th e co st is p erfectly in elastic to th e cap acity fo r g up to ap p ro x imately 2 00M b p s/k m 2. N o te, th o ug h, th at b uildin g o p erato r dep lo y ed W L AN n etw o rk s w ith full co v erag e p ro b ab ly is n o t feasib le co n siderin g th e g reat n umb er o f APs req uired so such a n etw o rk is a b it h y p o th etical. Hig h er traffi c den sities th an, e.g., 5 0M b p s/k m 2 are n o t lik ely to b e imp lemen ted usin g th e studied set o f radio access tech n o lo g ies o n ly. In stead th e results fo r h ig h er traffi c den sities sug g est h o w cellular an d W L AN lik e so lutio n s sh o uld b e imp ro v ed if traffi c in crease in th e future. In p articular, w e can see h o w imp o rtan t ex istin g assets (i.e. p rev io us dep lo y men t) o f mo b ile n etw o rk o p erato rs an d req uiremen ts o n traffi c den sity are fo r th e selectio n o f radio access tech n o lo g y. I V. PO T E N T I AL I M PR O V E M E N T S O F C U R R E N T SY ST E M S F o llo w in g th is an aly sis o f k ey p arameters to imp ro v e in HSDPA an d g if traffi c in creases w e w ill discuss b riefl y h o w such imp ro v emen ts co uld b e materializ ed. A. Capacity and coverage T o start w ith, cap acity (ag g reg ate th ro ug h p ut) p er site is o f co urse a majo r issue fo r macro cells if traffi c deman d in creases sig n ifi can tly. In th e lo n g run th is is p erh ap s easiest so lv ed v ia mo re sp ectrum b an dw idth w h ich, h o w ev er, usually co mes w ith a co n siderab le co st. Adv an ced tran smitter an d receiv ers tech - n iq ues, lik e M ultip le-in p ut-m ultip le-o utp ut (M IM O ) sy stems, are also p ro misin g. N aturally it is also b en efi cial to smo o th en o ut traffi c lo ad o v er time, e.g. usin g in ter-temp o ral p ricin g sch emes o r cach in g an d p re-fetch in g so lutio n s. M erely in creasin g cap acity p er site is n o t suffi cien t th o ug h. Also th e cell ran g e n eeds to b e main tain ed to p ro v ide co v erag e also fo r h ig h er p eak data rates. T h is req uires an imp ro v ed lin k b udg et w h ich, e.g., can b e ach iev ed th ro ug h M IM O, h ig h er masts, freq uen cy sp ectrum in lo w er b an ds, o r multih o p relay in g. Ab o v e all th e lin k b udg et is v ery critical if b ro adb an d data serv ices sh o uld b e p ro v ided in do o rs usin g o utdo o r B Ss. T h is is illustrated in F ig ure 3, w h ere w e h av e p lo tted th e th eo retical cell ran g e in up lin k fo r a co n v en tio n al W C DM A macro cell. A few ty p ical serv ices are dep icted in th e g rap h acco rdin g to stan dard lin k b udg ets p resen ted in [2 ]. Already at 1 44k b p s th e max imum cell ran g e is in th e o rder 7 00m fo r in do o r users. Hen ce, in creasin g p eak data rate to 1 M b p s reduces th e n o min al cell ran g e fro m almo st 7 00m to ap p ro x imately 3 5 0m sin ce th e lin k b udg et is lin early p ro p o rtio n al to th e data rate (all else eq ual). T o sup p o rt ev en h ig h er rates in a macro cellular n etw o rk th e lin k b udg et th erefo re n eeds to b e imp ro v ed. B. Cos t per acces s point C o n tin uin g to th e co st p er AP, w e h av e in Sectio n III-B co n sidered a reductio n o f th e to tal co st, in cludin g in v estmen ts an d run n in g co sts. In T ab le I th e main co st co effi cien ts fo r th e studied sy stems w ere listed. T h e b ase v alue h as b een e3 00k

117 101 C e ll ra n g e [m ] In c a r 12.2k b p s s p e e c h u s e r O u td o o r k b p s n o n re a ltim e d a ta u s e r In d o o r 14 4 k b p s re a ltim e d a ta u s e r 4 00 C O S T 23 1-W a lfis c h -Ik e g a m i C O S T 23 1-H a ta A llo w e d p a th lo s s [d B ] Fig. 3. Uplink range as a function of allowed path loss for urban WCDMA m acro cells. A few ty pical serv ices [2 ] are depicted in thegraph. for a single carrier H S DP A B S with an additional e1 0 k per cell and e39 k for g AP s. As discussed in [6 ], the cost structure for new m acro cells is today dom inated by costs for site acq uisition, buildout, installation and rental. In g howev er, last m ile - transm ission is a key contributor to the total infrastructure cost (followed by site rental). T his is based on an assum ption that a leased line is req uired per AP. L ow-cost transm ission alternativ es, e.g. wireless (m eshed) networks, could reduce O P E X to som e ex tent. B ut it does not change the conclusion that cov erage per g AP needs to be im prov ed (with retained capacity ) in order to lower the cost for operator deploy ed WL AN solutions. An alternativ e m ethod to prov ide indoor cov erage and capacity at a low cost could be to let users install AP s in their own prem ises that are open for access to all the operator s subscribers and roam ing partners, e.g. using a N etwork Franchise business m odel. T his solution seem s prom ising in particular for operators with a strong position also in fi x ed access. C. Positioning of next generation radio access technologies T he results also points at how a future 4 G radio interface targeted for urban env ironm ents could be differentiated with respect to current m ain stream technologies for wireless data connectiv ity. E x am ples of niches not cov ered well by today s sy stem s for urban deploy m ent are high capacity m icro cells, and long range WL AN access points. In both cases we assum e that the data rate at the cell border is signifi cantly higher than in 3G. Without m aking ex plicit assum ptions on req uired range and data rates, we can note that concepts sim ilar to the gaps indicated by this study already hav e been proposed in different contex ts; for ex am ple in the research program WIN N E R (recently initiated by the E U) and in sim ilar initiativ es. N um erical results on the infrastructure cost for a different 4 G concepts are presented in [3]. V. CO N CL US I O N S Multi-access networks are useful in order to lower infrastructure costs for operators in the long run if geographical traffi c density v aries strongly. Also in the short run, it can be benefi cial as a tem porary solution before im prov ed m acro cell networks and m ore freq uency spectrum are av ailable. As an ex am ple, we hav e looked in m ore detail into an operator deploy ed network with m acro cellular H S DP A base stations and IE E E g access points. For this sy stem the total infrastructure cost was q uantifi ed for a city center env ironm ent using a stochastic (log-norm al) m odel for heterogeneous traffi c density. It was shown that an H S DP A cell radius between m and m m inim iz e cost for av erage traffi c densities during busy hour of Mbps/km 2. T his approx im ately correspond to tim es the traffi c of priv ate v oice users today. For higher traffi c densities, either a v ery dense m acro cell lay er, or a large am ount of WL AN access points are needed and this is probably not feasible. We hav e also illustrated how elasticity of infrastructure cost can be used to effectiv ely analy z e what design param eters that are m ost im portant to im prov e in a m ulti-access wireless network. In the ex am ple with H S DP A and the capacity per m acro base station is m ore im portant to im prov e with m cell radius than g cov erage up to Mbps/km 2. H owev er, if H S DP A base stations are m ore sparsely deploy ed (8 0 0 m radius) the sam e cost sav ings can be achiev ed through increasing the range of g already at 5 0 Mbps/km 2. ACK N O WL E DG ME N T T his work has partly been sponsored by the S wedish Foundation for S trategic Research v ia the Affordable Wireless S erv ices and Infrastructure P roject. RE FE RE N CE S [1 ] CO S T 2 31 Final Report, av ailable at 31 / [2 ] H. H olm a and A. T oskala (editors), W CD M A for U M T S, T hird edition, J ohn Wiley & S ons, [3] A. Furuskär, M. Alm gren, and K. Johansson, An Infrastructure Cost E v aluation of S ingle- and Multi-Access N etworks with H eterogeneous User B ehav ior, in Proc. IE E E V ehicu lar T echnology Conference sp ring, May [4 ] A. Furuskär, Allocation of m ultiple serv ices in m ulti-access wireless sy stem s, in Proc. IE E E W ork shop on M ob ile and W ireless Com m u nications N etw ork, [5 ] U. G otz ner et al., S patial T raffi c Distribution in Cellular N etworks, in Proc. IE E E V ehicu lar T echnology Conference, [6 ] K. J ohansson, A. Furuskär, P. K arlsson, and J. Z ander, Relation between base station characteristics and cost structure in cellular networks, in Proc. IE E E Personal, Indoor and M ob ile R adio Com m u nications, S ept [7 ] J. K alliokulju et al., Radio Access S election for Multistandard T erm i- nals, IE E E Com m u nications M agaz ine, O ct [8 ] F. L oiz illon et al., F inal resu lts on seam less m ob ile IP serv ice p rov ision econom ics, IS T T O N IC Deliv erable num ber 1 1, O ct [9 ] N. N iebert et al., Am bient N etworks: An Architecture For Com m u- nication N etworks B ey ond 3G, IE E E W ireless Com m u nications, April [1 0 ] R. S. P indy ck and D. L. Rubinfeld, M icroeconom ics, Fifth edition, P rentice H all International, [1 1 ] D. P. Reed, T he Cost S tructure of P ersonal Com m unication S erv ices, IE E E Com m u nications M agaz ine, Apr [1 2 ] J. Z ander, O n the cost structure of future wideband wireless access, in Proc. IE E E V ehicu lar T echnology Conference,

118

119 Chapter 8 On the Cost Efficiency of User Deployed Access Points Integrated in Mobile Networks (Paper 4) Klas Johansson, In Proc. RVK 05, June

120

121 105 On th e c o s t e ffi c ie nc y o f u s e r d e p lo ye d a c c e s s p o ints inte g ra te d in m o b ile ne tw o rk s Klas J o h an sso n Wireles K TH, R oyal In s titu te of Tec h n ology, E lec tru m 4 1 8, S E K is ta, S wed en E m ail: k las j@ rad io.k th.s e Abstract O p en ac c es s to p r ivate, u s er d ep loy ed, ac c es s p oin ts is a p r om is in g c an d id ate for p r ovis ion in g of h ig h d ata r ates in p u b lic w ir eles s ac c es s s y s tem s. T h is d ep loy - m en t s tr ateg y s h ou ld es p ec ially b e an effi c ien t m eth od to s er ve in d oor u s er s in c ities w ith a s ig n ifi c an t p en etr ation of fi x ed b r oad b an d n etw or k s. We es tim ate th e in fr as tr u c tu r e c os t for d iffer en t m ix es of op er ator d ep loy ed (H S D P A m ac r o c ells an d g WL AN ) an d u s er d ep loy ed ac c es s p oin ts. T h e n etw or k is d im en s ion ed ac c or d in g to a s toc h as - tic (log -n or m ally d is tr ib u ted ) m od el for tr affi c d en s ity. R es u lts in d ic ate th at, w ith th e m od ellin g an d as s u m p tion s u s ed, u s er d ep loy ed ac c es s p oin ts low er th e in fr as tr u c tu r e c os t for tr affi c d en s ities ab ove 1 0 M b p s /k m 2 (1 0 tim es th e tr affi c of ty p ic al p r ivate s p eec h u s er s in a c ity c en ter ). M or eover, on ly a few p er c en t of th e s u b s c r ib er s n eed to in s tall op en ac c es s p oin ts to c over a s ig n ifi c an t fr ac tion of th e total tr affi c ter m in ated in d oor s. I. INTRODUCTION An in c reas in g availability of fi x ed broad ban d n etwork s, in c lu d in g d igital s u bs c riber lin es an d c able m od em s, an d th e d evelop m en t of Wireles s L AN tec h n ology will en able n ew d es ign s of p u blic wireles s ac c es s n etwork s. In th is s tu d y th e ec on om ic s of u s er d ep loyed loc al ac c es s p oin ts (AP s ) th at are als o op en for oth er s u bs c ribers an d roam in g p artn ers, is c on s id ered. M ore s p ec ifi c ally, we will estim ate th e in frastru c tu re c ost as a fu n c tion of traffi c d en s ity (area c ap ac ity) for d ifferen t m ix es of op erator d ep loyed bas e s tation s (B S ) an d u s er d ep loyed AP s. Fu rth erm ore, th e n u m ber of AP s n eed ed for s om e frac tion of c overed traffi c is ad d ressed. For th is p u rp os e a tec h n o-ec on om ic al m od el th at ac c ou n ts for both c ap ital an d op eration al ex p en d itu res (C AP E X an d O P E X ) is ap p lied. Th e n etwork is d i- m en sion ed ac c ord in g to average c ap ac ity an d ran ge p er ac c es s p oin t an d a s tatis tic al m od el for h eterogen eou s traffi c d en s ity is u s ed to c ap tu re geograp h ic al variation s in aggregate (d em an d ed ) th rou gh p u t. An ex am p le of a n etwork layou t with op erator d ep loyed m ac ro c ellu lar bas e s tation s an d ran d om ly p lac ed ac c es s p oin ts (AP s ) d ep loyed by u s ers is given in Figu re 1. P rivately own ed AP s th at are op en for p u blic ac c ess h ave p reviou s ly been p rop os ed in, e.g., [1,9,1 2 ]. In [1 2 ] it was c on c lu d ed th at ap p rox im ately twic e as m an y AP s are n eed ed to c over in d oor (offi c e) en viron m en ts with ran d om in s tead of p lan n ed p lac em en t. A s im ilar c as e is loc al WL AN p rovid ers p res en t in s p ec ifi c areas, e.g., airp orts an d h otels. Th es e op erators typ ic ally h ave roam in g agreem en ts with p u blic op erators wh o ac tu ally c h arge th e en d u s ers an d in tu rn p ay th es e loc al n etwork p rovid ers for p rovid in g ac c es s to th eir c u s tom ers [1 0 ]. A c learin g h ou se m ay also be u sed as an in term ed iator Fig. 1. An ex am p le o f a m ix ed n etw o rk w ith o p erato r d ep lo y ed m ac ro c ells an d u s er d ep lo y ed ac c es s p o in ts. to h an d le s ettlem en ts between loc al op erators an d th e u s ers h om e op erators [1 3 ]. In th is p ap er we will fu rth er in ves tigate h ow u s er d ep loyed in fras tru c tu re c ou ld be ex p loited by n etwork op erators th rou gh a fran c h is in g bu s in es s m od el, referred to as network fra nc h is ing. Network fran c h is in g an d th e AP c on c ep t is d es c ribed fu rth er in S ec tion II. A tec h n o-ec on om ic al m od el u s ed for estim atin g in frastru c tu re c osts is d esc ribed in S ec tion III, an d n u m eric al resu lts are p resen ted in S ec tion IV. C on c lu s ion s an d id eas for fu rth er res earc h in th is area are given in S ec tion V. II. NETWORK FRANCHISING AND USER DEPLOYED ACCESS POINTS It is well k n own th at th e ran ge for h igh d ata rate s ervic es is lim ited in a c on ven tion al c ellu lar system an d th is u ltim ately lead s to a h igh n u m ber of AP s if an y s ign ifi c an t area c overage is to be p rovid ed. With c u rren t tec h n ology ou td oor bas e s tation s h ave p roblem s in p rovid in g th e rec eived p ower level req u ired for in d oor c overage of h igh d ata rates. We c an th erefore assu m e th at su c h a s ys tem, c on s is tin g of a large n u m ber of AP s, m os t lik ely wou ld be too ex p en sive to bu ild in a c en traliz ed way an d th is h as been id en tifi ed as a c h allen ge for fu tu re p rovis ion in g of wireles s broad ban d s ervic es [1 4 ]. O th er au th ors h ave als o id en tifi ed th e n ec es s ary s h ort ran ge for h igh d ata rate wireless ac c ess in th e c on tex t of fou rth gen eration m obile n etwork s [8 ]. To res olve th is in tric ate is s u e we en vis age a d ec en - traliz ed bu sin ess m od el wh ere u sers, or c om p an ies with loc al p res en c e, in s tall AP s an d c on n ec t th em to ex is tin g fi x ed broad ban d n etwork s. For n etwork p rovid ers, fran - c h is in g p rovid es an op p ortu n ity to lower in s tallation an d

122 106 Chapter 8. User Deployed Access Points (Paper 4) operational c osts. Th is oc c u rs bec au se th e u sers d eploy AP s th em selves in th eir own fac ilities and u se th eir own power and th eir ex isting fi x ed broad band c onnec tion. Th ere are natu rally c osts for th e u ser to install th e AP, to g ive it spac e, for elec tric power, m aintenanc e, and for th e broad band c onnec tion. H owever, we ex pec t th at u sers are d eploying a private WL AN ac c ess point anyway, so th ese c osts will be h id d en for th e u ser and, th erefore, rem oved from th e operator. A. T h e n e tw o r k fra n c h is in g bu s in e s s m o d e l G enerally speaking, franc h ising refers to a two-layered bu siness m od el wh ere a franc h iser offers brand nam e and c ore fu nc tions su c h as proc u rem ent, bac k-offi c e, and IT su pport to a larg e nu m ber of affi liates, c alled franc h isees. Th ese franc h isees norm ally h ave to pay a fee for u sing th e brand and su pport fu nc tionality and follow ru les for, e.g., store selec tion, servic e and prod u c t q u alities set u p by th e franc h iser. H owever, m ost loc al profi ts are kept by th e loc al franc h isee. Im plem entations of th is bu siness m od el c an be fou nd in, e.g., th e retail ind u stry. With network franc h ising both parties benefi t from th e arrang em ent. Th e operator obtains ac c essibility to AP s provid ing c h eap, h ig h -c apac ity wireless ac c ess wh ereas th e u ser g ets an AP and som e c om pensation by th e operator. In prac tic e we ex pec t th at operators will c om - pensate th e AP owner th rou g h bu nd ling of d ifferent servic es, su c h as fi x ed broad band, su bsid iz ed ac c ess box es, and wireless ac c ess wh en th e u ser is in oth er loc ations. N atu rally th e ac tu al d esig n of th e offering will d epend on th e types of provid ers, wh ic h allows for a nu m ber of new ways of pac kag ing and d istribu ting th e d eploym ent; th is issu e is ou tsid e th e sc ope of th is paper. Th is bu siness m od el c ou ld be of interest to m obile network operators with lim ited spec tru m and /or poor ind oor c overag e, or broad band provid ers th at wou ld like to ex ploit th eir fi x ed network by offering wireless ac c ess (ind oors). A prom ising bu siness c ase wou ld be operators th at provid e fi x ed broad band ac c ess and m obile su bsc riptions as a M obile Virtu al N etwork O perator s (M VN O ). Th ese M VN O s c ou ld offer ind oor and loc al c overag e by m eans of franc h ising of AP s, th ereby only u tiliz ing th e m obile network as a c om plem ent (prim arily for ou td oor and m obile u sers). N ote, h owever, th at with ou t reg u latory interventions operators with ou t th eir own fi x ed broad band ac c ess wou ld probably su ffer from h ig h inter-c onnec tion fees for th e last m ile. In any c ase, th e proposed bu siness m od el fosters m ore c om petition in ac c ess networks, wh ic h h as been an im portant objec tive of telec om reg u lation au th orities for a long tim e [2 ]. B. U s e r d e p lo y e d a c c e s s p o in ts U ser d eployed AP s c an su pport a sing le or m u ltiple rad io ac c ess tec h nolog ies. M oreover we assu m e th at th e AP s will, wh en installed, be self-c onfi g u ring and au tom atic ally integ rate itself into th e operator s network (th e spec ifi c network arc h itec tu re is h owever not with in th e sc ope of th is stu d y). F u rth erm ore, th e U niversal M obile Ac c ess (U M A) tec h nolog y c u rrently being d eveloped will allow u sers with m u lti-m od e h and sets to ac c ess m obile servic es also via WL AN [1 1 ]. Th is c ou ld be an im portant step in im plem enting th e proposed bu siness m od el in prac tic e. In fac t, in th e following we will assu m e th at eac h AP h as sim ilar c h arac teristic s as IE E E b. III. SYSTEM MODELLING, ASSUMPTIONS, AND PERFORMANCE MEASURES A tec h no-ec onom ic al m od el previou sly presented in [3 ] will be u sed to estim ate th e infrastru c tu re c ost for a m o- bile network. Th e m od el is, for th e sake of c onvenienc e, d esc ribed briefl y nex t. A. N e tw o r k d im e n s io n in g a n d tra ffi c m o d e llin g A h eu ristic m eth od is u sed to d im ension th e rad io ac c ess network. In operator d eployed system s m ac ro c ells are d eployed fi rst on an h ex ag onal g rid and d im ensioned ac c ord ing to traffi c d em and. If th e traffi c ex c eed s th e m ax im u m c apac ity in som e c ell, WL AN ac c ess points are d eployed wh ere need ed. Th ese are plac ed in areas with h ig h est traffi c (i.e., prim arily in h ot spots). In th e m ix ed c ase with both u ser- and operator-d eployed base stations, th e AP s are fi rst d eployed rand om ly ac c ord ing to a 2 D -P oisson proc ess (i.e., th ey are u niform ly d istribu ted ). R esid u al traffi c th at c an not be c overed by u ser d eployed AP s are th en alloc ated to operator d eployed base stations (ag ain, with m ac ro c ells fi rst). Traffi c d ensity is m od elled with a log -norm al, spatially c orrelated, stoc h astic variable over th e servic e area (1 0 x 1 0 km ) wh ic h is fu rth er d ivid ed into sam ples of 2 0 x 2 0 m ; see th e ex am ple in F ig u re 2. A stand ard d eviation of 7 d B h as been assu m ed and th e c orrelation d istanc e is m [3 ]. In all nu m eric al ex am ples th e averag e popu lation d ensity is inh abitants/km 2, c orrespond ing to a c ity c enter. Th e operator is assu m ed to h ave a 3 0 % m arket sh are and servic e penetration is 9 0 %. H enc e, th e nu m ber of su bsc ribers is u sers/km 2 on averag e (loc ally th is is m u c h h ig h er). B. Ac c e s s p o in t p e r fo r m a n c e a n d c o s t a s s u m p tio n s AP s are c h arac teriz ed by d ifferent c ell rad ii, c apac ities, and c osts; see Table I. Th e c apac ity c oeffi c ients for Fig. 2. Ex am p le o f traffi c d e n s ity g e n e rate d w ith th e h e te ro g e n e o u s traffi c m o d e l fo r a s e rv ic e are a o f 1 0 x 1 0 k m T ra ffic D e n s ity [lo g 10 (Mbps/km 2 )]

123 g as s u m es n o c o-c h an n el in terferen c e wh ereas H S D P A d oes, d u e to th e c ellu lar d ep loym en t an d lim ited freq u en c y s p ec tru m ; h ere we as s u m e 3 c arriers x 5M H z (15M H z in total) for d own lin k. N otic e th at th e m axim u m c ap ac ity for H S D P A an d g is s im ilar, 22.5 an d 22M bp s, so th e AP with lowest c ost p er tran sm itted bit is es s en tially d eterm in ed by th e geograp h ic al d is tribu tion of traffi c. Th e c ell rad iu s of H S D P A is 1000m for traffi c d en s ities below 5M bp s /k m 2, 800m between 5 an d 20M bp s /k m 2, an d 4 00m for d en s ities above th at [6 ]. TAB L E I ACCESS POINT CHARACTERISTICS. HSDPA g AP R a d iu s m 4 0 m 5 0 m C a p a c ity [3-9 ] x 2.5 Mb p s 2 2 Mb p s 5 Mb p s C o s t c o e ffi c ie n t 1 (5 5 % /4 5 % ) 0.13 (3 % /9 7 % ) (C APE X /O PE X ) p e r c e ll C os t c oeffi c ien ts in c lu d e both C AP E X an d O P E X, wh ere O P E X is c alc u lated in p res en t valu e over a 10- year p eriod u s in g a 10% d is c ou n t rate (s ee fu rth er [4 ]). For th e s ak e of s im p lic ity th e n etwork is d im en s ion ed to c arry th e s am e traffi c d u rin g th e n etwork life s p an. Th e c os t for H S D P A is bas ed on th e es tim ate d erived in [4 ] for m ac ro c ell W C D M A bas e s tation s, wh ic h in tu rn was based on estim ates p rovid ed by th e G artn er G rou p an d [7 ]. In th e n u m eric al exam p les we h ave as s u m ed th at a m ac ro B S c os ts e300k. C os ts for rad io n etwork c on trollers an d elec tric al p ower h ave been included as op p os ed to th e es tim ates in [4 ]. Table I s u m m ariz es th e c os t s tru c tu re in term s of C AP E X an d O P E X an d th e ad d ition al c ost for extra c ells (d efi n ed as a c arrier freq u en c y an d s ec tor) in H S D P A. An in c u m ben t op erator th at alread y h as sites for legac y system s in stalled m ay reu se m ost of th ese sites an d we assu m e th at th is lowers th e c os t for H S D P A B S s by 25%. For g n ew c ost estim ates h ave been d ed u c ted based on [7 ]. U ser d ep loyed AP s are assu m ed to c ost e2000 in total, in c lu d in g c os ts for eq u ip m en t, c u s tom er c are, etc., an d som e reven u e sh arin g with th e AP own er. N otic e th at th is is bas ed on ou r as s u m p tion s an d n ot on em p iric al d ata, s in c e th e c on c ep t h as n ot been im p lem en ted in p rac tic e yet. C. In fra s tru c tu re c o s t m ea s u res Th e bas ic m eas u re for c os t effi c ien c y u s ed is th e in fra s - tru c tu re c o s t p er G B p er m o n th. In d oin g th is m ap p in g we assu m e th at 0.6 % of th e m on th ly traffi c is c arried d u rin g eac h bu s y h ou r, wh ic h rou gh ly c orres p on d s to th e traffi c p attern in c u rren t c ellu lar s ys tem s, an d th at th e n etwork is d im en s ion ed ac c ord in g to average aggregate th rou gh p u t (p er s am p le area). As a referen c e c as e we will u s e th e traffi c of a typ ic al p rivate voic e telep h on y u s er, gen eratin g 20m E of traffi c at 10k bp s d u rin g bu s y h ou r. Th is c orres p on d s to 200bp s th rou gh p u t on average d u rin g th e bu s y h ou r. In th is m od el th e ac tu al traffi c m ix, m obility, etc., are th u s exogen ou s. Yet, th e res u lts an d c on c lu s ion s s h ou ld h old for all traffi c m ixes th at fall with in th e p erform an c e p aram eters given in Table I. P e rc e n ta g e o f tra ffic c o v e re d T y p ic a l fra c tio n o f tra ffic te rm in a te d in d o o rs in to d a y 's m o b ile s y s te m s m E v o ic e tra ffic x v o ic e 2 00 x v o ic e P e rc e n ta g e o f s u b s c rib e rs w ith A P s Fig. 3. P e rc e n tag e o f traffi c c o ve re d as a fu n c tio n o f th e p e rc e n t o f u s e rs w ith an o p e n AP, fo r d iffe re n t d ata vo lu m e s. IV. INFRASTRUCTURE COST ESTIMATES In th is s ec tion a few n u m eric al exam p les of th e c os t of a m obile in fras tru c tu re with an d with ou t AP s are p rovid ed, u s in g th e m od els ou tlin ed above. A. N u m eric a l res u lts Figu re 3 s h ows th e p erc en tage of traffi c c overed with AP s as a fu n c tion of th e p erc en tage of s u bs c ribers eq u ip p ed with AP s. For low traffi c d en s ities, h ere u p to ap p roxim ately 100 x voic e traffi c, th e AP s h ave overc ap ac ity an d th e p lot ac tu ally c orres p on d s to th e frac tion of th e s ervic e area c overed. As traffi c p er u s er in c reases, th e frac tion of traffi c served by AP s at a given AP p en etration (d en s ity) d ec reas es. B as ed on th es e res u lts we will as s u m e th at 1, 2, or 4 % of th e u s ers h ave AP s in th e followin g exam p les. Th is im p lies th at ap p roxim ately 30, 4 0, an d 7 0% of th e total traffi c is c overed by AP s. In p rac tic e, it is u n lik ely th at all traffi c c an be rou ted via u s er d ep loyed AP s s in c e fu ll ou td oor c overage (in c lu d in g m obile u s ers ) c an n ot be exp ec ted. A c om m on as s u m p tion tod ay is th at arou n d % of th e traffi c in m obile n etwork s is term in ated in d oors s o th is c ou ld be seen as an u p p er lim it (as d ep ic ted in 3). Th e total in fras tru c tu re c os t for m u lti-ac c es s n etwork s c on s is tin g of op erator d ep loyed H S D P A an d g ac c es s p oin ts in c om bin ation with AP s in s talled by n on e, 1%, 2%, an d 4 % of th e u s ers is d ep ic ted in Figu re 4. Th e p lot s h ows th e in fras tru c tu re c os t p er s u bs c riber as a fu n c tion of average traffi c d en s ity. At low traffi c volu m es an op erator d ep loyed n etwork yield s th e lowes t c os t, wh ereas for h igh er traffi c in tegratin g AP s in th e n etwork d ec reases th e average c ost p er tran sm itted G B. Th e s lop e of th e c u rves an d c ros s -over p oin ts n atu rally d ep en d on th e as s u m p tion s on p erform an c e, c os ts, an d reven u e s h arin g. Th e n u m ber am ou n t of ac c es s p oin ts an d H S D P A tran s c eivers (TR X s ) is s u m m ariz ed in Table II for s everal levels of AP in stallation s an d two volu m es of traffi c. For 2 exam p le at 10 x voic e traffi c, in trod u c in g 100 AP s /k m will yield ap p roxim ately th e sam e c ost as an op erator d ep loyed n etwork. H owever, th e n u m ber of H S D P A

124 108 Chapter 8. User Deployed Access Points (Paper 4) M o n th ly In fra s tru c tu re C o s t [E u ro /G B ] H S + 11g H S + 11g + A P 1% H S + 11g + A P 2% H S + 11g + A P 4 % TAB LE II THE NUMBER OF TRANSCEIVERS AND ACCESS POINTS REQUIRED PER SERVED KM 2 IN A CITY CENTER. Subscribers w ith AP 0 % 1 % 2 % 4 % 10 x vo ic e tr a ffi c H SD P A T R X s g AP s AP s x vo ic e tr a ffi c H SD P A T R X s g AP s AP s T ra ffic D e n s ity [M b p s /k m 2 ] Fig. 4. Mo n th ly c o st p er tran sm itted GB as a fu n c tio n o f average traffi c d en sity. Th e so lid lin e is fo r an o p erato r d ep lo y ed m u lti-ac c ess n etwo rk with H SDPA an d g. Th e o th er lin es rep resen t d ifferen t p erc en tages o f su b sc rib ers with APs. transceivers and op erator d ep loyed g access p oints are d ecreased sig nifi cantly. At a traffi c of x voice th is is even clearer and in th is case th e network of u ser d ep loyed AP s also bring s a lower cost, as can be seen in Fig u re 4. B. D is c u s s io n o f re s u lts S ince m any strateg ic factors h ave been left ou t (e.g. d em and, Q os, and p reviou s assets of th e op erator) in th is stu d y we can not m ak e g eneral recom m end ations on op tim u m d ep loym ent strateg ies. Yet, som e interesting asp ects can be h ig h lig h ted from a cost p ersp ective. First, th e econom ics of scale is interesting to note. I.e., th at increasing traffi c volu m es h as a d im inish ing increm ental cost. Th is h as p reviou sly been q u estioned for cellu lar network s and u sed as an arg u m ent for novel bu siness m od els and d ep loym ent strateg ies su ch as u ser d ep loyed infrastru ctu re [1 ], [1 4 ]. Th ese resu lts ind icate th at h ig h er d ata rates can actu ally be p rovid ed at a sig nifi cantly lower increm ental cost by m eans of an u ser d ep loyed infrastru ctu re. Th is occu rs even with a sig nifi cant p ortion of revenu e sh aring (in th is exam p le ap p roxim ately e1 0 0 p er year p er AP wh ich in p ractice is q u ite a su bstantial su bsid y). S econd ly, th e sh are of investm ents relative to ru nning costs (CAP E X d ivid ed by O P E X ) is m u ch lower th an for a conventional cellu lar network. Th is typ ically lowers th e risk and is a virtu e of u ser d ep loyed wireless infrastru ctu re th at is an interesting top ic for fu rth er research. V. CONCLUSIONS Th e infrastru ctu re cost h as been estim ated for d ifferent d ensities of op erator and u ser d ep loyed access p oints. Resu lts ind icate th at it is su ffi cient if a few p ercent of th e su bscribers of a m ajor op erator (with 3 0 % m ark et sh are) in an u rban area install op en access p oints to lower th e infrastru ctu re cost sig nifi cantly. With th e u sed m od elling and assu m p tions, cost saving s are sig nifi cant at traffi c levels above 1 0 M bp s/k m 2. Th is wou ld ap p roxim ately corresp ond to ten tim es th e traffi c of p rivate voice u sers. To com p ensate th e access p oint owners for allowing p u blic access to th eir internet connections we h ave assu m ed a su bstantial revenu e sh aring (in th e ord er of e1 0 0 p er year p er access p oint). Th u s, introd u cing u ser d ep loyed access p oints th at are op en for access also to oth er su bscribers seem s to be a cost effi cient m eth od to p rovid e h ig h d ata rates in p u blic wireless access system s. ACKNOWLEDGMENT Th is work was sp onsored by th e S wed ish Fou nd ation for S trateg ic Research via th e Afford able Wireless S ervices and Infrastru ctu re (AWS I) P rog ram and th e L ow Cost Infrastru ctu re (L CI) P roject. Th e au th or wou ld also lik e to ack nowled g e th e contribu tions of D r. And ers Fu ru sk är (E ricsson Research ) and Jonas L ind (S tock h olm S ch ool of E conom ics) to th is work. REFERENCES [1 ] A. B ria et al., 4 th -Gen eratio n Wireless In frastru c tu res: Sc en ario s an d Researc h Ch allen ges, IEEE Pe rs on a l C om m u n ic a tion s, Dec [2 ] M. Cave, S. Maju m d ar, an d I. Vo gelsan g (ed ito rs), H an d b o o k o f Telec o m m u n ic atio n s Ec o n o m ic s, Elsevier Sc ien c e, [3 ] A. Fu ru sk är, M. Alm gren, an d K. J o h an sso n, An In frastru c tu re Co st Evalu atio n o f Sin gle- an d Mu lti-ac c ess Netwo rk s with H etero gen eo u s User B eh avio r, in Proc. IEEE V T C S p rin g [4 ] K. J o h an sso n, A. Fu ru sk är, P. Karlsso n, an d J. Z an d er, Relatio n b etween b ase statio n c h arac teristic s an d c o st stru c tu re in c ellu lar n etwo rk s, in Proc. IEEE PIM R C [5 ] K. J o h an sso n, et al. In tegratin g User Dep lo y ed Lo c al Ac c ess Po in ts in a Mo b ile Op erato r s Netwo rk, In Proc. WWR F M e e t- in g n r. 1 2, [6 ] K. J o h an sso n an d A. Fu ru sk är, Co st effi c ien t c ap ac ity ex p an sio n strategies u sin g m u lti-ac c ess n etwo rk s, In Proc. IEEE V T C S p rin g [7 ] F. Lo iz illo n et al., F in a l re s u lts on s e a m le s s m ob ile IP s e rv ic e p rov is ion e c on om ic s, n o t p u b lish ed, IST TONIC Deliverab le n u m b er 1 1, Oc t [8 ] W. Mo h r, R. Lü d er, an d K-H. Mö h rm an n, Data Rate Estim ates, Ran ge Calc u latio n s an d Sp ec tru m Dem an d fo r New Elem en ts o f Sy stem s B ey o n d IMT , in Proc. IEEE WPM C [9 ] J. M. Pereira, Fo u rth Gen eratio n : No w it is Perso n al!, in Proc. IEEE PIM R C [1 0 ] O. Tirla et al., Ac c o u n tin g m an agem en t in h etero gen eo u s m o b ile ac c ess n etwo rk s: th e MIND ap p ro ac h, in Proc. IEEE WPM C [1 1 ] UMA Tec h n o lo gy, h ttp :// m atec h n o lo gy.o rg/ [1 2 ] Matth ias Un b eh au n, O n th e D e s ig n a n d D e p loy m e n t of L ow - c os t Wire le s s In fra s tru c tu re, Do c to ral Dissertatio n, Ro y al In stitu te o f Tec h n o lo gy, Dep artm en t o f Sign als, Sen so rs an d Sy stem s, [1 3 ] S. Wo lters an d H. Lu ed iger, Reso u rc e b ro k erage in fu tu re wired an d wireless n etwo rk s - WH Y LESS.COM, n o t p u b lish ed, availab le at h ttp :// y less.o rg/p u b lic /wp 3.h tm. [1 4 ] J. Z an d er, On th e c o st stru c tu re o f fu tu re wid eb an d wireless ac c ess, in Proc. IEEE V T C

125 Chapter 9 Radio Resource Management in Roaming Based Multi-Operator WCDMA networks (Paper 5) Klas Johansson, Martin Kristensson, and Uwe Schwarz, In Proc. IEEE VTC2004 Spring, May

126

127 111 Radio Resource Management in Roaming Based Multi-O p erator W C D MA N etw ork s K las J oh ansson Radio C ommunication Sy stems L ab oratory D ep t. of Signals, Sensors & Sy stems Roy al Institute of T ech nology S ST O C K H O L M, Sw eden E mail: k lasj@ radio.k th.se Martin K ristensson and U w e Sch w arz N ok ia N etw ork s Säterinp ortti, P L N ok ia G roup, F IN L A N D E mail: {martin.k ristensson, uw e.sch w arz }@ nok ia.com Abstract Infrastructure sharing and Mobile Virtual Network O p erators (MVNO ) are becom ing m ore and m ore com m on in today s m obile networks. In both cases the subscribers of m ultip le op erators connect to the sam e radio access network v ia, e.g., roam ing based m ethods. D ep ending on how m uch each op erator p ay s, they should then be guaranteed a certain cap acity in the shared network. T his p ap er discusses different solutions for how the radio resources in such a roam ing based m ulti-op erator W C D MA network m ay be allocated to the sharing op erators. O ne p articular m ethod based on R adio R esource Managem ent (R R M) with nonp reem p tiv e p riority q ueuing in the adm ission control is p resented in detail. T he m ethod seem s to p rov ide an attractiv e tradeoff between fairness and total sy stem cap acity. I. I N T RO D U C T I O N Sh aring th e radio access netw ork (RA N ) h as b ecome p op - ular among U MT S mob ile op erators during th e recent y ears. T h e main reason is p erh ap s to low er th e inv estment costs, b ut it could also b e to reduce op erating costs in th e long run. In p articular in rural areas, w h ere cov erage driv es th e total netw ork cost, th is h as also to some ex tent b een imp lemented in p ractice. Moreov er, op erators w ith out a 3 G license may act as Mob ile V irtual N etw ork op erators, and th ereb y ex tend th ere serv ice offering b y roaming into anoth er op erators netw ork. F or th is p urp ose, similar functionality as w ith international roaming can b e utiliz ed to inter-connect th e op erators netw ork s. A nd, th is p ap er inv estigates meth ods for h ow to allocate radio resources in such roaming b ased multi-op erator W C D MA netw ork s. T ech nically, w ith roaming based sharing, an op erator access anoth er op erators RA N indirectly v ia th e core netw ork s. T h is imp lies th at multip le op erators fully sh are th e same RA N, and th ere is h ence a p otential need for radio resource control b etw een th e op erators. N ormally th e op erators sh are th e same carrier(s), b ut it is also p ossib le to use dedicated carriers. Besides roaming b ased sh aring, w h ich is th e top ic of th is p ap er, th ere are also oth er sh aring solutions. T h e main categories th at w e see today are R A N sharing and site sharing. T h e th ree group s of solutions imp ly different lev els of sh aring, w h ich is dep icted in F ig. 1. W ith RA N b ased sh aring th e op erators h av e dedicated carriers b ut sh are netw ork elements up to and including th e Core network R a d io N etwork Controller B S B S B S B S R oa m ing b a s ed s h a ring R A N s h a ring S ite s h a ring F ig. 1. T h e fi gure illustrates w h at lev els of th e U MT S netw ork arch itecture th at different sh aring meth ods relate to. radio netw ork controller (RN C ), or p ossib ly only th e b ase stations. T h e adv antage is th at op erators, alth ough sh aring signifi cant p arts of th e RA N, still h av e a h igh degree of indep endence. T h e isolation b etw een th e sh aring op erators is ev en b etter w ith site sh aring. In th is case th ey only sh are for ex amp le th e p lace w h ere th e b ase station is located or p h y sical eq uip ment, such as for ex amp le antennas and p ow er sup p lies. W ith th ose infrastructure sh aring meth ods it is p ossib le to imp lement a numb er of different use cases. F or ex amp le, geograp h ical sh aring w h ere op erators p rov ide cov erage in different p arts of a country, or a mix of ow n netw ork s in urb an areas and a common sh ared netw ork in low p op ulated areas. Sh ared netw ork s h av e recently b een under inv estigation in 3 G P P ; see [6 ] and [7 ]. T h ough, RRM algorith ms are mainly p rop rietary and h ence not treated in 3 G P P. In th e research community sh ared netw ork s h av e b een discussed in for ex amp le [1 ], [5 ] and [4 ], b ut th e RRM asp ects in roaming b ased sh aring h av e not attracted much research interest y et. T h e outline of th e p ap er is as follow s. In Section II w e b riefl y discuss different w ay s for sh aring th e radio resources b etw een multip le op erators w ith roaming b ased sh aring. T h en, in Section III a meth od to dy namically allocate cap acity is p resented in more detail. T h e p erformance of th is algorith m is analy z ed w ith simp le simulations in Section IV, and conclusions are draw n in Section V.

128 112 Chapter 9. Fair Resource Sharing (Paper 5) II. ME T H O D S F O R A L L O C A T IN G R A D IO R E S O U R C E S W IT H R O A MIN G B A S E D S H A R IN G H o w m u c h o f th e ra d io n e tw o rk c a p a c ity th a t e a c h s h a rin g p a rtn e r h a s th e rig h t to u s e w ith ro a m in g b a s e d s h a rin g is c o m m o n ly s p e c ifi e d in a S e rv ic e L e v e l A g re e m e n t (S L A ). A n o p e ra to r th a t fo llo w s its te rm s in th e S L A s h o u ld re c e iv e th e a g re e d Q u a lity o f S e rv ic e (Q o S ) le v e ls ; th is e v e n if th e o th e r o p e ra to rs try to u tiliz e m o re c a p a c ity th a n a g re e d. T h e o n ly w a y fo r a n o p e ra to r to o b ta in m o re c a p a c ity s h o u ld th u s b e to e ith e r p a y fo r a la rg e r s h a re, o r in v e s t in m o re c a p a c ity. In p ra c tic e th is m e a n s th e th e ra d io re s o u rc e m u s t b e s h a re d in a c o n tro lle d w a y b e tw e e n th e o p e ra to rs. A n d, th e ra d io re s o u rc e s c a n in p rin c ip le b e a llo c a te d b y : u s in g d e d ic a te d c a rrie rs fo r e a c h o p e ra to r, a llo c a tin g a fi x e d c a p a c ity s h a re fo r e a c h o p e ra to r p e r c a rrie r, o r d y n a m ic a lly p rio ritiz e o p e ra to rs (w ith in o n e o r m u ltip le c a rrie rs ). N e x t w e w ill d is c rib e th o s e m e th o d s b rie fl y, a n d d is c u s s th e a p p lic a b ility fo r d iffe re n t u s e c a s e s. A. Dedicated carriers In th is c a s e th e o p e ra to rs s h a re fo r e x a m p le th e b a s e s ta tio n s, th e tra n s m is s io n n e tw o rk a n d th e ra d io n e tw o rk c o n tro lle r, b u t th e y e a c h h a v e th e ir d e d ic a te d c a rrie r la y e r. D e d ic a te d c a rrie rs re s u lt in g o o d in te r-o p e ra to r is o la tio n, b u t th e d e d ic a te d c a rrie rs a ls o re s u lt in u n n e c e s s a ry h ig h in v e s tm e n t c o s ts in s o m e s c e n a rio s. E s p e c ia lly in ru ra l a re a s th e c a p a c ity o f a s in g le -c a rrie r W C D MA n e tw o rk c o u ld b e w e ll e n o u g h to c o v e r th e n e e d s fo r m u ltip le o p e ra to rs. B. F ix ed cap acity sh ares p er carrier W ith a d v a n c e d R R M fu n c tio n a lity, a fi x e d fra c tio n o f th e c e ll c a p a c ity c a n b e re s e rv e d fo r e a c h o p e ra to r a n d o n ly o n e c a rrie r is th u s re q u ire d. T h is a p p ro a c h re s u lts in lo w e r in v e s t- m e n t c o s ts a s c o m p a re d to d e d ic a te d c a rrie rs (in p a rtic u la r in c o v e ra g e lim ite d a re a s ) a n d it s till p ro v id e s p e rfe c t fa ir s h a rin g o f th e a v a ila b le c a p a c ity. H o w e v e r, it c o u ld a ls o le a d to a lo s s in to ta l s y s te m c a p a c ity a s c o m p a re d to fu lly s h a re d c a rrie rs d u e to a d e c re a s e d s ta tis tic a l m u ltip le x in g g a in. A n e x a m p le o f th e tru n k in g e ffi c ie n c y is g iv e n in F ig. 2. H e re th e a v e ra g e c h a n n e l u tiliz a tio n η is d e p ic te d a s a fu n c tio n o f th e n u m b e r o f a v a ila b le c h a n n e ls p e r c e ll C. F o r a to le ra b le b lo c k in g p ro b a b ility B max = 5% (c a lc u la te d a c c o rd in g to th e w e ll k n o w n E rla n g -B fo rm u la, s e e e.g. [3 ]), th e a v e ra g e c h a n n e l u tiliz a tio n is h e re d e fi n e d a s η = O C, (1 ) w h e re th e o ffe re d lo a d O is th e to ta l o ffe re d lo a d g iv e n in E rla n g a n d th e n u m b e r o f c h a n n e ls C is a c o n s ta n t. W ith C = 8 0 c h a n n e ls a v a ila b le p e r c e ll th e to ta l c a p a c ity is re d u c e d w ith 1 0 % a lre a d y w ith tw o o p e ra to rs s h a rin g th e c a p a c ity in e q u a lly s iz e d s h a re s. A n d w ith fo u r s h a rin g o p e ra to rs th e lo s s is u p to 2 0 %. N o tic e th a t th is a ls o b rin g s m o re c o s ts w h ic h w o u ld c o n tra d ic t w ith th e m a in p u rp o s e o f A v e ra g e c h a n n e l u tiliz a tio n, η % re d u c tio n 10% re d u c tio n N u m b e r o f c h a n n e ls p e r c e ll, C F ig. 2. C h a n n e l u tiliz a tio n η a s a fu n c tio n o f th e to ta l n u m b e r o f c h a n n e ls C p e r c e ll w ith 5 % a v e ra g e b lo c k in g p ro b a b ility. s h a rin g a W C D MA n e tw o rk. T h u s, w ith fi x e d c a p a c ity s h a re s, th e c o s t o p e ra to rs h a v e to p a y fo r th e fa ir c a p a c ity a llo c a tio n is m o s t lik e ly h ig h e r th a n th e v a lu e a d d e d. C. Dy n am ical p rio ritiz atio n o f o p erato rs w ith in o n e o r m u l- tip le carriers A s th e s o lu tio n s d is c u s s e d a b o v e a ll le a d to a s ig n ifi c a n t lo s s in to ta l s y s te m c a p a c ity o r c o s t e ffi c ie n c y, a d y n a m ic a l p rio ritiz a tio n o f o p e ra to rs b a s e d o n th e c u rre n t lo a d is p re fe r- a b le. F o r th is p u rp o s e s ta n d a rd R R M fu n c tio n a lity s u c h a s a d m is s io n c o n tro l a n d p a c k e t s c h e d u lin g c a n b e u tiliz e d. A d m is s io n c o n tro l is in th is c o n te x t re s p o n s ib le fo r a d m is - s io n o f n e w c o n n e c tio n s (b o th p a c k e t a n d c irc u it s w itc h e d ), w h e re a s p a c k e t s c h e d u lin g a d a p tiv e ly a d ju s ts th e b it ra te o f c o n n e c te d n o n re a l-tim e b e a re rs [2 ]. F o r th e s a k e o f s im p lic ity, w e o n ly tre a t c irc u it s w itc h e d tra ffi c in th is p a p e r. H o w e v e r, it s h o u ld b e s tra ig h tfo rw a rd to e x te n d th e s o lu tio n to h a n d le a ls o p a c k e t s w itc h e d tra ffi c. Mo re o v e r, o n ly a s in g le -s e rv ic e s y s te m is tre a te d. T h e p rio ritiz a tio n c a n im p le m e n te d w ith p rio rity q u e u in g in th e a d m is s io n c o n tro l. E a c h c o n n e c tio n b e lo n g in g to a n o p e ra to r s h o u ld th e n re c e iv e a p rio rity c a lc u la te d b a s e d o n th a t o p e ra to rs c u rre n t lo a d re la tiv e to th e ir a g re e d m in im u m c a p a c ity, s o th e p rio rity le v e l re fl e c ts h o w m u c h e a c h o p e ra to r h a s u tiliz e d its a g re e d c a p a c ity s h a re.. A n o th e r d e s ig n d e c is io n is w h e th e r o r n o t to u s e p re - e m p tio n (a llo w in g fo r re m o v a l o f e x is tin g c o n n e c tio n s to m a k e s p a c e fo r a n e w re q u e s t). P re e m p tio n w o u ld p e r d e fi n itio n in c re a s e th e p ro b a b ility o f d ro p p in g a c tiv e c o n n e c tio n s. In g e n e ra l th is is c o n s id e re d to b e th e m o s t im p o rta n t q u a lity o f s e rv ic e m e a s u re fo r c irc u it-s w itc h e d s e rv ic e s (lik e v o ic e te le p h o n y ) a n d p re e m p tio n is h e n c e n o t p re fe ra b le. H a v in g s a id th a t, th e m e th o d w ith n o n -p re e m p tiv e p rio rity q u e u in g in a d m is s io n c o n tro l s e e m s to b e th e m o s t p ro m is in g s o lu tio n to th e s ta te d p ro b le m a n d w e w ill fo c u s o n th is m e th o d in th e s e q u e l o f th is p a p e r.

129 113 III. MU L T I-O P E R A T O R A D MIS S IO N C O N T R O L W IT H N O N -P R E E MP T IV E P R IO R IT Y Q U E U E IN G A. System model and performance measures F o r th e fu rh e r a n a ly s is a s ta n d a rd P o is s o n tra ffi c m o d e l w ill b e u s e d (s e e e.g. [3 ]). T h e to ta l o ffe re d lo a d p e r o p e ra to r i is d e n o te d O i, a n d is d e fi n e d a s O i = λ it, (2 ) w h e re λ i is th e a v e ra g e a rriv a l ra te o f n e w c o n n e c tio n s fo r o p e ra to r i a n d T is th e a v e ra g e d u ra tio n p e r c o n n e c tio n. T h e to ta l o ffe re d lo a d is th e n g iv e n b y N O = O i, (3 ) i=1 a s s u m in g th a t N o p e ra to rs s h a re th e n e tw o rk. T h e to ta l n u m b e r o f c h a n n e ls p e r c e ll C is s till m o d e le d a s c o n s ta n t. A lth o u g h it is w e ll k n o w n th a t th is is n o t th e c a s e in a W C D MA s y s te m (s e e e.g. [2 ], w e b e lie v e th is in itia l a s s e s s m e n t is n o t im p ro v e d s ig n ifi c a n tly b y m o d e lin g th is in m o re d e ta il. E a c h o p e ra to r is a s s ig n e d a m in im u m c a p a c ity le v e l p e r c e ll, C i, h e rin c o rre s p o n d in g to th e n u m b e r o f c h a n n e ls a n o p e ra to r is g u a ra n te e d a c c o rd in g to th e S L A. A n e w c o n n e c tio n re q u e s t is q u e u e d u n til th e re a re re s o u rc e s a v a ila b le. H o w e v e r, if th e w a itin g tim e T d e x c e e d s a c e rta in th re s h o ld T m a x th e re q u e s t is b lo c k e d. T h e b lo c k in g p ro b a b ility fo r a n o p e ra to r, B i, is th u s d e fi n e d a s B i = P r (T d > T m a x ) P r (U s e r b e lo n g s to o p e ra to r i). (4 ) F u rth e rm o re, th e lo a d p e r o p e ra to r L i is s im p ly g iv e n b y th e to ta l n u m b e r o f a llo c a te d c h a n n e ls fo r th a t o p e ra to r a t a g iv e n p o in t in tim e. B. Alg orith m description A n o v e rv ie w o f th e a lg o rith m u s e d fo r a d m is s io n c o n tro l w ith n o n -p re e m p tiv e p rio rity q u e u in g is d e p ic te d in F ig. 3. T h e p rio rity le v e l o f e a c h o p e ra to r, P i, is d e fi n e d a s P i = Ci L i, (5 ) s o th a t o p e ra to rs w ith a lo a d L i lo w e r th a n th e a g re e d m in im u m c a p a c ity C i re c e iv e s a h ig h p rio rity. T h e q u e u in g m a n a g e m e n t o u tlin e d h e re c a n b e im p le m e n te d in c o n ju n c tio n w ith, e.g., th e p e rio d ic a l a d m is s io n c o n tro l fu n c tio n a lity d e s c rib e d in [2 ] a n d it c o n s is ts o f th e fo llo w in g s te p s. 1 ) A n e w c o n n e c tio n re q u e s t th a t a rriv e s w h e n th e s y s te m is c o n g e s te d is p u t in th e q u e u e. 2 ) T h e q u e u e is p e rio d ic a lly s o rte d in a n d e s c e n d in g o rd e r a c c o rd in g to P i. T h e n e a c h o p e ra to r s c o n n e c tio n s a re s o rte d g ro u p -w is e in a d e s c e n d in g o rd e r b a s e d o n T d. C o n s e q u e n tly, th e o p e ra to r w ith h ig h e s t P i w ill b e s e rv e d fi rs t a n d e a c h o p e ra to r s c o n n e c tio n re q u e s ts a re s e rv e d in a fi rs t-in -fi rs t-o u t (F IF O ) m a n n e r re la tiv e to e a c h o th e r. Put new connection req ues t(s ) in q ueue S ort q ueue: 1 ) P i 2 ) T d C h a nnel a v a ila b le? Y es A lloca te ch a nnel N o N o T d > T m a x? Y es C onnection b lock ed! F ig. 3. F lo w c h a rt o f p e rio d ic a l a d m is s io n c o n tro l w ith n o n -p re e m p tiv e p rio rity q u e u in g. 3 ) N o rm a l a d m is s io n c o n tro l is p e rfo rm e d fo r e a c h c o n n e c - tio n re q u e s t in th e q u e u e in p rio ritiz e d o rd e r. If e n o u g h re s o u rc e s a re a v a ila b le, a c h a n n e l is a llo c a te d. 4 ) C o n n e c tio n re q u e s ts fo r w h ic h T d > T m a x a re b lo c k e d a n d re m o v e d fro m th e q u e u e. C. P erformance measures H o w e ffe c tiv e th e d iffe re n tia tio n in b lo c k in g p ro b a b ility is d e p e n d s o n th e p ro b a b ility th a t e n o u g h re s o u rc e s a re re le a s e d b e fo re th e m a x im u m a llo w e d w a itin g tim e T m a x is re a c h e d a n d a c o n n e c tio n h a s to b e b lo c k e d. T h is s h o u ld m a in ly b e a fu n c tio n o f u s e r b it ra te s, a v e ra g e c o n n e c tio n d u ra tio n s a n d th e m a x im u m a llo w e d q u e u in g tim e. H e n c e, th e p e rfo rm a n c e c a n b e e v a lu a te d b y o b s e rv in g th e o p e ra to r s p e c ifi c b lo c k in g p ro b a b ility B i. If th e a lg o rith m p e rfo rm s w e ll, B i B m a x fo r O i < C i. (6 ) T h a t is, B i s h o u ld b e b e lo w a c e rta in th re s h o ld B m a x u n til th e o p e ra to r re a c h e s its a g re e d m in im u m c a p a c ity C i. A n d a s a c o n s e q u e n c e, a t h ig h to ta l o ffe re d lo a d O th e b lo c k in g p ro b a b ility w ill b e h ig h e r fo r o p e ra to rs th a t h a v e e x c e e d e d th e ir lo a d s h a re. IV. S IMU L A T IO N S A N D R E S U L T S T h e p e rfo rm a n c e o f th e a lg o rith m o u tlin e d in S e c tio n III h a s b e e n in v e s tig a te d b y m e a n s o f s im p le q u e u in g s im u la tio n s w ith tw o o p e ra to rs (N = 2). F irs t, g e n e ra l s im u la tio n a s s u m p tio n s a n d m o d e ls a re d e s c rib e d. T h e n a fe w e x a m p le s a re g iv e n to s h o w h o w th e a lg o rith m c o u ld fu n c tio n in d iffe re n t s c e n a rio s. A. G eneral assumptions and traffi c models A s in g le c e ll h a s b e e n s im u la te d in w h ic h a ll c o n n e c tio n s h a v e th e s a m e b it ra te. E a c h o p e ra to r h a s p rio ritiz e d a c c e s s to a to ta l n u m b e r o f C i c h a n n e ls. A n d, if n o t s ta te d o th e rw is e, b o th o p e ra to rs h a v e th e s a m e g u a ra n te e d c a p a c ity s o th a t C 1 = C 2 = C 2. (7 )

130 114 Chapter 9. Fair Resource Sharing (Paper 5) TABLE I SIM U LATED SER V IC ES Se rv ic e Sp e e c h V id e o s tre a m in g C h a n n e ls p e r c e ll C Allo w e d q u e u in g tim e Tmax [s ] 5 s 1 5 s Av e ra g e c o n n e c tio n tim e s s D a ta ra te k b p s 6 4 k b p s A c o n n e c tio n is a d m itte d if th e re is a t le a s t o n e c h a n n e l a v a ila b le, th a t is if N L i < C, (8 ) i=1 a n d b lo c k e d if n o c h a n n e l is re le a s e d b e fo re th e m a x im u m a llo w e d w a itin g tim e T m a x is e x c e e d e d. Th e p rio rity le v e l P i is u p d a te d c o n tin u o u s ly w ith o u t a n y te m p o ra l a v e ra g in g a n d is th e s a m e fo r a ll c o n n e c tio n s b e lo n g in g to th e s a m e o p e ra to r. Th e to ta l n u m b e r o f c h a n n e ls C a n d th e m a x im u m w a itin g tim e T m a x a re s e rv ic e s p e c ifi c a n d th e s a m e in a ll s im u la tio n s ; s e e Ta b le I. N o te a ls o th a t, a c c o rd in g to th e P o is s o n tra ffi c m o d e l u s e d, b o th th e in te r-a rriv a l tim e s o f c o n n e c tio n re q u e s ts a n d c o n n e c tio n d u ra tio n s a re e x p o n e n tia lly d is trib u te d. B. Blocking probability for a speech service Th e b lo c k in g p ro b a b ility o f a n o p e ra to r i = 1 is d e p ic te d in F ig. 4 a s fu n c tio n o f its o ffe re d lo a d O 1. Th is h a s b e e n s im u la te d fo r a fe w d iffe re n t v a lu e s o f o ffe re d lo a d fo r th e s e c o n d o p e ra to r, O 2. N o w, a c c o rd in g to (6 ), th e b lo c k in g p ro b a b ility o f th e s tu d ie d o p e ra to r B 1 s h o u ld b e k e p t b e lo w s o m e th re s h o ld B m a x a s lo n g a s L 1 < C 1. F ig. 4 s h o w s th a t th e a lg o rith m p e rfo rm s w e ll fo r a s p e e c h s e rv ic e. Th is is s im p ly d u e to th a t th e re a re q u ite m a n y c h a n n e ls (8 0 ) a v a ila b le p e r c a rrie r. Th u s, th e re is a h ig h lik e lih o o d th a t a c h a n n e l is re le a s e d b e fo re th e m a x im u m a llo w e d q u e u in g tim e T m a x is e x c e e d e d. A fi x e d c a p a c ity a llo c a tio n o f 4 0 c h a n n e ls d e d ic a te d fo r o p e ra to r 1 is d e p ic te d a s a re fe re n c e c a s e. In te re s tin g to n o te is th a t fo r a lo w lo a d th e b lo c k in g p ro b a b ility is h ig h e r w ith d y n a m ic a l p rio ritiz a tio n th a n w ith d e d ic a te d c a p a c ity. H o w - e v e r, a s s o o n a s th e lo a d in c re a s e th e d y n a m ic a l p rio ritiz a tio n o u tp e rfo rm s a fi x e d a llo c a tio n o f c h a n n e ls. Th e a p p ro x im a te g a in in te rm s o f o ffe re d lo a d fo r a to le ra b le b lo c k in g p ro b a b ility B m a x = 5 % is s u m m a riz e d in Ta b le II. Th e g a in a s c o m p a re d to a d e d ic a te d c a p a c ity s c h e m e is n a tu ra lly h ig h e r w h e n th e to ta l s y s te m lo a d is lo w. TABLE II GAIN R ELATIV E TO A F IX ED ALLO C ATIO N O F 4 0 SP EEC H C H AN N ELS W ITH Bmax = 5% TO LER ABLE BLO C K IN G P R O BABILITY. O2 [Erl] O1 [Erl] Ga in 5 % 1 0 % 3 0 % 8 0 % F in a lly, w e c a n a ls o s e e in F ig. 4 th a t, w h e n th e o ffe re d lo a d fo r th e s e c o n d o p e ra to r O 2 in c re a s e, s o d o e s u n fo rtu n a te ly a ls o th e b lo c k in g p ro b a b ility o f o p e ra to r 1 (B 1). H o w e v e r, in th is c a s e B 1 c a n s till b e k e p t b e lo w B m a x = 5 %. Th u s, w e c o n c lu d e th e a lg o rith m p e rfo rm s w e ll fo r a W C D M A s y s te m w ith s p e e c h u s e rs a n d th is s h o u ld a ls o h o ld fo r a n y c irc u it s w itc h e d v id e o a n d d a ta s e rv ic e w ith a m o d e ra te d a ta ra te. C. Blocking probability for a stream ing vid eo service As th e b it ra te in c re a s e, th e to ta l n u m b e r o f c h a n n e ls a v a ila b le p e r c a rrie r d e c re a s e a n d th e n o n p re m p tiv e p rio rity q u e u in g a lg o rith m s h o u ld c o n s e q u e n tly p e rfo rm w o rs e. Th is is a ls o in d ic a te d b y th e s im u la te d re s u lts in F ig. 5 fo r a v id e o s tre a m in g s e rv ic e. In th is c a s e th e b lo c k in g p ro b a b ility o f th e fi rs t o p e ra to r B 1 c a n n o t b e k e p t b e lo w B m a x = 5 % w h e n th e lo a d is h ig h fo r th e o th e r o p e ra to r, a n d th e re is h e n c e a lo s s in c a p a c ity B lo c k in g p ro b a b ility fo r o p e ra to r 1, B F ix e d re s e rv a tio n O 2 = 80 E rl O 2 = 60 E rl O 2 = 40 E rl O 2 = 20 E rl B lo c k in g p ro b a b ility fo r o p e ra to r 1, B F ix e d re s e rv a tio n O 2 = 14 E rl O 2 = 10 E rl O 2 = 6 E rl O 2 = 2 E rl Offe re d lo a d fo r o p e ra to r 1, O 1 [E rl] F ig. 4. Th e b lo c k in g p ro b a b ility o f o p e ra to r 1 (B1) fo r a s p e e c h s e rv ic e a s a fu n c tio n o f o ffe re d lo a d (O1) fo r d iffe re n t le v e ls o f lo a d fo r th e o th e r o p e ra to r (O2). A fi x e d re s o u rc e a llo c a tio n o f 4 0 c h a n n e ls p e r o p e ra to r is d e p ic te d a s a re fe re n c e Offe re d lo a d fo r o p e ra to r 1, O 1 [E rl] F ig. 5. Th e b lo c k in g p ro b a b ility o f o p e ra to r 1 (B1) fo r a v id e o s tre a m in g s e rv ic e a s a fu n c tio n o f o ffe re d lo a d (O1) fo r d iffe re n t le v e ls o f lo a d fo r th e o th e r o p e ra to r (O2). A fi x e d re s o u rc e a llo c a tio n o f 8 c h a n n e ls p e r o p e ra to r is d e p ic te d a s a re fe re n c e.

131 115 as compared to the reference case with 8 dedicated channels; see T ab le III. T his despite the fact that we hav e increased the max imu m allowed waiting time T max to 1 5 s (instead of 5 s which was u sed for the speech serv ice). H owev er, with a more moderate load for the second operator, for ex ample O 2 = 6 E rl, the alg orithm fu nctions well also in this case. T AB L E III GAI N RE L AT I V E T O A F I X E D AL L O C AT I O N O F 8 S T RE AM I N G C H AN N E L S W I T H 5% T O L E RAB L E B L O C K I N G P RO B AB I L I T Y. O2 [E rl] O1 [E rl] Gain -6 0 % -6 % 4 0 % % T hese resu lts indicate that for circu it switched serv ices with hig h data rates, one cou ld consider to slowly adju st the minimu m capacity g u aranteed per operator C i according to the av erag e demand. F or pack et switched serv ices, howev er, it is simpler to mu ltiplex b etween u sers and the same ty pe of prob lem shou ld hence not occu r for su ch traffi c. D. Coexistence of operators with low and high minimum agreed capacity per cell In the prev iou s simu lations, b oth operators had the same g u aranteed capacity lev el C i. It is also of interest to u nderstand if an operator with a low capacity share can coex ist with an operator that has a hig her reserv ed capacity and a larg er share of the offered traffi c load. In F ig. 6 we show a few ex amples for a speech serv ice where C 1 = 2 0 and C 2 = 60. T he load for operator 1, O 1, was increased linearly for two different lev els of O 2; namely 6 0 and E rlang. F rom these resu lts, it can b e noticed that the b lock ing prob ab ility can b e k ept low also for operators with a small fraction of the total capacity. H owev er, the b lock ing prob - ab ility of the operator with low load is increased slig htly as the load of the second operator increase. O n the other hand, B 2 B 1, so the b lock ing prob ab ility of the b ig g er operator is sig nifi cantly hig her. T hu s, the alg orithm clearly differentiates the b lock ing prob ab ility of the two operators. S o from a fairness perspectiv e, the alg orithm performs well also in this case and one operator can not cannib aliz e on the other operators resou rces withou t a penalty in terms of increased b lock ing prob ab ility. V. C O N C L U S I O N S haring radio resou rces in a mu lti-operator W C D M A network is b est done with a comb ination of serv ice lev el ag reements (S L A) and su pporting fu nctionality for fair resou rce sharing. If the operators will ex perience clear drawb ack s when they v iolate an S L A, they will b e forced to follow the ag reement, b u y capacity from another sharing partner or pay for a capacity ex pansion of the shared network. T his paper ou tlined mu ltiple technical solu tions to the radio resou rce-sharing prob lem and describ ed one of them, admission control with non-preemptiv e priority q u eu ing, in O p e ra to r s p e c ific b lo c k in g p ro b a b ility, B i B 2 fo r O 2 = 100 E rl B 1 fo r O 2 = 100 E rl B 2 fo r O 2 = 6 0 E rl B 1 fo r O 2 = 6 0 E rl O ffe re d lo a d fo r o p e ra to r 1, O 1 [E rl] F ig. 6. C oex istence of operators with sig nifi cantly different traffi c load and max imu m capacity lev el Ci. H ere, C1 = 2 0 and C2 = 6 0 channels. T he offered load for operator 1 is v aried and depicted for two different lev els of offered load for operator 2 (O2 = 6 0 and E rlang ). detail. It was shown that this method mak es it possib le to differentiate the b lock ing prob ab ility of operators sharing the same carrier(s), in particu lar for serv ices with modest data rates (lik e v oice telephony ). F or fu rther work it wou ld b e interesting to analy z e the performance with pack et switched connections, and the implications of av erag ing load measu rements. AC K N O W L E D GM E N T T he au thors wou ld lik e to thank P rof. J ens Z ander (Roy al Institu te of T echnolog y, S weden) and P reb en M og ensen (N ok ia N etwork s, D enmark ) for their v alu ab le comments and feedb ack on this work. RE F E RE N C E S [1 ] A. B artlett and N. N. J ack son, N etwork P lanning C onsiderations for N etwork S haring in U M T S, In P roceedings of 3 G M ob ile Communication T echnologies, M ay 8-1 0, [2 ] H. H olma and A. T osk ala, W CDM A F O R U M T S R adio A ccess for T hird G eneration M ob ile Communications, John W iley & S ons. [3 ] N g C hee H ock, Q ueueing M odelling and F undamentals, J ohn W iley & S ons, [4 ] J.H arno, 3 G B u siness C ase su ccessfu lness within the C onstraints S et b y C ompetition, Reg u lation and Alternativ e T echnolog ies, in P roceedings of F IT CE Congress, S eptemb er 4-7, [5 ] J. A. V illag e, K. P. W orrall and D. I. C rawford, 3 G S hared Infrastru c- tu re, In P roceedings of 3 G M ob ile Communication T echnologies, M ay 8-1 0, [6 ] 3 rd Generation P artnership P roject (3 GP P ), T echnical S pecifi cation Grou p (T S G) RAN 3, S hared N etwork S upport in Connected M ode (R elease 5 ), T echnical report R , v ersion 1.0.0, S eptemb er [7 ] 3 rd Generation P artnership P roject (3 GP P ), T echnical S pecifi cation Grou p S erv ices and S y stem Aspects, S erv ice A spects and R eq uirements for N etwork S haring (R elease 6 ), T echnical report , v ersion 6.0.0, D ecemb er

132

133 Chapter 10 An Estimation of the Achievable User Throughput with National Roaming (Paper 6) Johan Hultell and Klas Johansson, Submitted to IEEE VTC2006 Spring. 117

134

135 119 An E s tim a tio n o f th e Ac h ie va b le U s e r T h ro u g h p u t w ith N a tio na l R o a m ing Johan H u lte ll, and K las Johans s on Wireless@ K TH, Th e R oyal In stitu te of Tec h n ology E lec tru m 4 1 8, S K ista, S wed en E m ail: {joh an.h u ltell, klasj}@ rad io.kth.se Abstract N a tion a l roa m in g, th a t is a llowin g u s ers to a c c es s n etwork s of m u ltip le d om es tic op era tor s, wa s d is c u s s ed a lrea d y d u rin g d evelop m en t of fi rs t a n d s ec on d g en era tion m obile s y s - tem s. For m obile voic e s ervic es, h owever, th e op era tors a fford ed to bu ild n etwork s with (a lm os t) fu ll c overa g e a lon e. T h e ben efi ts with n a tion a l roa m in g, in term s of in c rea s ed c overa g e, tru n k in g effi c ien c y a n d lowered ris k ex p os u re, d id c on s eq u en tly n ot ex c eed th e d ra wba c k s a s s oc ia ted with in c rea s ed op era tor c oop era tion. M otiva ted by th e c on s id era ble in ves tm en ts for p rovid in g c overa g e for m obile d a ta servic es, a n d en a bled by th e c om m on ra d io res ou rc e m a n a g em en t p rop os ed for s y s tem s B ey on d 3 G, th is p a p er 1 eva lu a tes th e ben efi ts with n a tion a l roa m in g for bes t effort d a ta s ervic es. T h e res u lts s h ow th a t n a tion a l roa m in g m ore th a n d ou bles th e d a ta ra tes for u s ers a t th e c ell bord er. T h is g a in is m otiva ted by red u c ed p a th loss a n d lowered in terferen c e levels. A rou n d h a lf of th e p oten tia l g a in is c a p tu red a lrea d y with n ea rly c o-s ited ba s e s ta tion s, wh ic h s u g g es ts th a t th e ben efi ts for op era tors to c oord in a te th eir ba s e s ta tion s ite p la n s a re lim ited. I. INTRODUCTION D esp ite th e in trod u c tion of ad van c ed tran sm ission an d p ac ket sc h ed u lin g tec h n iq u es, ex istin g m obile d ata n etworks wou ld req u ire sign ifi c an t, an d h en c e c ostly, u p grad es in ord er to su p p ort h igh er d ata rates with wid e area c overage. C on seq u en tly, th e vast m ajority of op erators h ave c h osen to p ostp on e th e n ec essary n etwork u p grad es u n til c on su m er d em an d bec om es m ore p ron ou n c ed. Alth ou gh th is at a fi rst glan c e m igh t seem sou n d, it is d efi n itely n ot th e c ase sin c e su p p ortin g h igh -en d u sers ( early ad op ters ) h as p roven to be a c ru c ial en abler for later reac h in g a m ass-m arket [1 ]. O n e m eth od to in c rease c overage for h igh er d ata rates wou ld be to en able u sers to roam between m u ltip le op erators with in a c ou n try (h en c eforth referred to as national roam ing ) u n til d em an d for h igh -sp eed servic es ju stifi es a n etwork ex p an sion. Th e m eth od was d isc u ssed alread y d u rin g d evelop m en t of th e fi rst an d sec on d gen eration system s, bu t for m obile voic e servic es op erators afford ed to bu ild fu ll c overage n etworks alon e. S u p p ortin g broad ban d d ata servic es are h owever assoc iated with m u c h larger in vestm en ts an d, as alon g as d em an d is resilien t, risks. M oreover th ere will, c om p ared to voic e servic es, be fewer sim u ltan eou s u sers an d th u s larger sc op e for statistic al m u ltip lex in g. All of th is yet again m akes n ation al roam in g an in terestin g altern ative. 1 Th is wo rk h a s b e e n c o n d u c te d with in th e No ve l A c c e s s P ro vis io n in g (NA P ) p ro je c t, c o -fu n d e d b y th e S we d is h A g e n c y fo r In n o va tio n S ys te m s (VINNOVA ). With th is said, th e c h ief ad van tage with n ation al roam in g is th at on ly sm all in vestm en ts are n eed ed wh ereas th e m ain d rawbac k is th at it m igh t h arm c om p etition between th e op - erators an d th at th e in volved n etworks n eed to be c om p atible. H en c e, it is of p aram ou n t im p ortan c e th at both th e ex c h an ge of bu sin ess sen sitive in form ation (e.g. load m easu rem en ts) is kep t at m in im u m level, wh ic h we d isc u ssed in [2 ], an d th at th e en ablin g fu n c tion ality rem ain sim p le so th at ex it barriers assoc iated with n etwork sh arin g agreem en ts are sm all [4 ]. In p rac tise th is m ean s th at ex istin g fu n c tion ality sh ou ld be reu sed as far as p ossible wh ereas n ew m ec h an ism s ou gh t to be d esign ed in a m od u lar fash ion [3 ]. A sim ilar c on c ep t is geograp h ic al sh arin g of wireless in frastru c tu re. It h as p reviou sly been p rop osed as a m eth od to lower in frastru c tu re c osts an d savin gs in th e ord er of 1 0 p erc en t of a m obile op erator s total c ost h as been rep orted [4 ]-[5 ]. Th is is, h owever, p rim arily a m eth od for lowerin g roll-ou t c osts for n ew rad io ac c ess tec h n ologies wh ereas n ation al roam in g rath er is abou t d eferrin g in vestm en ts by sh arin g ex istin g, an d th u s alread y d ep loyed, in frastru c tu re. Th e p roblem of alloc atin g u sers to th e ap p rop riate base station in h eterogen eou s m u lti-n etwork en viron m en ts h as attrac ted sign ifi c an t atten tion in th e literatu re [6 ]-[1 2 ]. D yn am ic resou rc e m an agem en t of m u ltip le wireless n etworks, ac ross rad io ac c ess tec h n ologies an d bu sin ess bou n d aries, is also a m ain th em e in th e in tegrated E U p rojec t Am bien t N etworks [1 0 ]. A th orou gh in vestigation of h ow u sers sh ou ld be alloc ated with in on e op erator s m u ltiac c ess n etwork was c on d u c ted in [8 ] an d it was sh own th at sign ifi c an t gain s c ou ld be ac h ieved in a c om bin ed G S M /E D G E an d WC D M A sc en ario. Also [9 ] ex am in ed gain s th at c ou ld be attain ed by ex p loitin g th e c oex isten c e of m u ltip le system s to in c rease th e tru n kin g effi c ien c y. It was c on c lu d ed th at c om m on rad io resou rc e m an agem en t with fou r in tegrated n etworks d ou bles th e in terac tive p ac ket c ap ac ity. O th er stu d ies, su c h as [1 1 ]-[1 2 ] h ave id en tifi ed c om p lem en tary m etric s su c h as m on etary c ost, u ser p referen c es, etc., th at c ou ld be c on sid ered wh en alloc atin g u sers in m u lti-op erator en viron m en ts. In th is p ap er we will in vestigate wh at th rou gh p u t op erators c an ex p ec t with n ation al roam in g for u sers loc ated at th e c ell bord er as well as in average. M oreover we stu d y if, an d to wh at ex ten t, th ese gain s d ep en d on th e m ic rosc op ic d iversity ord er an d th e relative site loc ation s of th e in volved system s (d egree of overlap ). Th e stu d y is lim ited to u p lin k tran sm ission, sin c e th at typ ic ally lim its c overage in c ellu lar system s.

136 Chapter 10. Throughput with National Roaming (Paper 6) II. SYSTEM MODEL B es id es in trod u cin g a m od el for q u an tifyin g th e d egree of overlap between in volved s ys tem s, th is s ection p res en ts th e u s ed p rop agation m od el an d m u lti-acces s s ch em es. A. U s e r a n d Tra ffi c B e h a v io r A m u lti-op erator en viron m en t with J op erators is in ves - tigated. T h rou gh ou t th e p ap er all active u s ers are s tation ary an d u n iform ly d is tribu ted. T h is res u lts in th at th e traffi c load for op erator j is P ois s on d is tribu ted with ex p ected valu e ω j (m eas u red in u s ers p er k m 2 ). Moreover we, for s im p licity reas on s, as s u m e th at all u s ers h ave fu ll bu ffers an d d em an d best effort traffi c d 3 d 1 7d 2 r c B. M u lti-o p e ra to r E n v iro n m e n t a n d B a s e S ta tio n D e p lo y m e n t N ation al roam in g en ables u s ers to con n ect to an y of th e J coop eratin g op erators base station s. For th e sak e of sim p licity all op erators are as s u m ed to d ep loy bas e s tation s, eq u ip p ed with m u ltip le th ree-s ector an ten n as, in a h ex agon al grid (with corres p on d in g bas e s tation d en s ity ρ j). E ach op erator h as on e carrier freq u en cy, wh ich is u sed in every cell (reu se factor 1). T h e coverage an d cap acity gain s attain ed from n ation al roam in g d ep en d on th e em p loyed h an d off algorith m, an d th e relative p osition of th e sites belon gin g to th e op erators. T h e latter is cap tu red by th e in te r-o p e ra to r s ite d is ta n c e d, wh ich we d efi n e as th e m in im u m d is tan ce between ad jacen t s ites belon gin g to d ifferen t op erators. T h u s for a s cen ario with J op erators d = m in ( ) d 1, d 2,..., d ( J. (1) 2) 2 Alth ou gh d 1 d 2... d ( J in gen eral it can be s h own 2) th at d ep loym en ts wh ere d 1 = d 2 =... = d ( j ex is t if J belon g 2) to th e so-called m agic n u m bers. For a th ree-op erator scen ario, th e relation sh ip of th e op erators site location s are given as ( xi y i ) ( ) ( ) x0 co s (π/6 ) = + d y 0 ( 1) i, (2 ) s in (π/6 ) wh ere i = 1, 2 an d (x 0, y 0) T is th e p os ition of th e referen ce n etwork. Figu re 1 d ep icts an ex am p le wh ere d = r c an d in Section IV th e in ter-op erator s ite d is tan ce will be u s ed to con trol th e d egree of overlap between th e n etwork s. C. P ro p a g a tio n M o d e l G iven a u s er p os ition, th e s lowly varyin g (ex p ected ) com - p on en t of th e p ath gain between u s er k an d bas e s tation l can be written in logarith m ic scale as µ kl = G kl + G s [d B ]. (3 ) G kl d en otes th e d eterm in is tic p art of th e p ath gain an d is h erein d es cribed by th e C O ST H ata m od el [13 ]. U s in g s tan d ard p aram eters for an ou td oor u rban s ettin g (i.e. bas e 2 It c an b e s h o w n th at th is is p o s s ib le if th e n u m b er o f n etw o rk s J b elo n g to th e s o -c alled m ag ic n u m b ers {1,3,4,7,9,...} F ig. 1. A n ex am p le o f th e b as e s tatio n d ep lo y m en t fo r a th ree-o p erato r s c en ario an d in ter-o p erato r s ite d is tan c e d = rc= m. It s h o u ld b e n o ted th at th e m ax im u m valu e o f d is p erio d ic w ith a p erio d 2 3rc s tation h eigh t of 3 0 m, u s er h eigh t of 1.5 m an d a 3 d B correction factor) it red u ces to G kl = ( log 10 f c log 10 r kl) [d B ], (4 ) wh ere f c is th e carrier freq u en cy in MH z an d r kl is th e d is tan ce between th e bas e s tation an d th e m obile term in al. T h e s econ d p art of E q. (3 ) corres p on d s to th e s p atially correlated log-n orm al s h ad ow fad in g (s low fad in g). With in th is s tu d y it is as s u m ed to h ave a s tan d ard d eviation σ s = 8d B an d a d ecorrelation d is tan ce of 2 0 m [14 ]-[15 ]. B es id es th e s low fad in g d es cribed above, th e an ten n a elem en ts on th e bas e s tation s are as s u m ed to be ex p os ed to in d ep en d en t fl at R ayleigh fad in g with ex p ected valu e eq u alin g µ kl. T h u s th e p ath gain between a u s er an d its corres p on d in g bas e s tation can be d es cribed by M id en tically, in d ep en d en tly R ayleigh d is tribu ted ran d om variables wh ere M rep res en ts th e n u m ber of an ten n a elem en ts. D. M u lti-ac c e s s S c h e m e We s tu d y two s ch ed u lin g m eth od s for s h arin g th e d ata ch an n el an d d u e to s im p licity reas on s we as s u m e th at u s ers on ly can con n ect to on e bas e s tation at a tim e u s in g T D MA. H en ce we d o n ot accou n t for p oten tial d ivers ity gain s from s oft h an d over (or rath er, in th e cas e of T D MA, fas t cell s election ). T h e fi rs t m u lti-acces s s ch em e con s is ts of a rou n d robin (R R ) s ch ed u ler wh ere u s ers in a p articu lar cell s h are th e ch an n el in a ran d om fas h ion. O n average th ou gh, all u s ers in th e cell obtain th e ch an n el an eq u al am ou n t of th e tim e ( tim e-h op p in g ). C om p ared to u s in g a cyclicly rep eated s ch ed u le th is yield s s m ooth er in terferen ce s tatis tics. In th e s econ d m eth od a p rop ortion fair (P F) s ch ed u ler is u s ed. C on trary to th e R R s ch ed u ler, wh ich ign ore th e ch an n el con d ition s, th e P F s ch ed u ler ex p loit th e fas t fad in g variation s an d as s ign s th e ch an n el to th e u s er ex p erien cin g th e bes t relative ch an n el q u ality. For bas e s tation l, th e ch an n el q u ality associated with u ser k is m easu red as

137 121 Q kl = M m=1 G klm µ kl [lin], (5 ) w h ere G lkm c o rres p o nd s to ind ep end ent ex p o nentially d is - trib u ted variab les. Th e nu m erato r es tim ates th e ins tantaneo u s c h annel q u ality and th e d eno m inato r d es c rib es th e ex p ec ted c h annel q u ality. B as ed o n th is m eas u re, eac h b as e s tatio n s c h ed u le th e u s er fu lfi lling k = argm ax Q lk k kl (6 ) w h ere k l is th e s et o f u s ers c o nnec ted to b as e s tatio n l. Fo r b o th sc h ed u ling strategies th e average transm it p o w er is lim ited to th e m ax im u m o f an average valu e o f P = 2 4 d B m and a p eak valu e o f P m a x = 3 3 d B m. C o ns eq u ently a u s er c o nnec ted to a b as e s tatio n w ith N 1 o th er s im u ltaneo u s u s ers u tiliz e a trans m it p o w er given b y E. D a ta R a te Es tim a tio n P k = m in ( P N, Pm a x ) [lin]. (7 ) As s u m ing th at u s er k b elo ngs to th e s et o f u s ers th at are s c h ed u led to trans m it d u ring tim e-s lo t k t, th e rec eived s ignalto -interferenc e (SIR ) ratio at antenna elem ent m b elo nging to b as e s tatio n l c an b e w ritten as P kg klm γ klm =, (8 ) i=kt\k PiGim + N0W w h ere N 0W is th e rec eived no is e p o w er (m o d elled as AWG N w ith c o ns tant s p ec tral d ens ity ). As s u m ing s y nc h ro no u s m ax i- m u m ratio c o m b ining (MR C ), th e resu lting instantaneo u s SIR is o b tained as M Γ kl = γ klm. (9 ) m=1 U s ing th es e SIR valu es, th e d ata rates are es tim ated th ro u gh th e Sh anno n m o d el w ith a m ax im u m p eak b it rate R m a x ac c o rd ing to R kl = m in(r m a x, W lo g 2 (1 + Γ kl)), (1 0 ) w h ere R ma x is th e m ax im u m p eak th ro u gh p u t th at th e m o d - u latio n and c o d ing s c h em es s u p p o rt. Th is is s et to eq u al th e 9 0 th p erc entile o f p eak th ro u gh p u t (p er tim e s lo t) in th e s ingle o p erato r c as e w ith fo u r antennas. In th is c as e R ma x = 2 0 Mb p s (es tim ated th ro u gh s im u latio ns ). D u e to o u r id eal as s u m p tio ns and m o d elling th is is h igh er th an in p rac tic al s y s tem s. F. M in im u m Pa th -L o s s B a s e S ta tio n S e le c tio n We as s u m e th at u s ers c o nnec t to th e b as e s tatio n as s o c iated w ith th e h igh es t lo ng-term average p ath gain µ kl. C o ns e- q u ently u s er k c o nnec ts to b as e s tatio n l w h ere l = argm ax µ kl l l (1 1 ) and l is th e s et o f b as e s tatio n th at th e u s er c an s elec t. TAB LE I SYSTEM PARAMETERS Pa ra m e te r Va lu e Po p u la tio n d e n s ity [k m 2 ] # Op e ra to rs 3 Slo t d u ra tio n [m s ] 2 Co rre la tio n d is ta n c e [m ] 20 Sta n d a rd d e via tio n (s h a d o w fa d in g ) [d B ] 8 Sh a d o w fa d in g c o rre la tio n b e twe e n b a s e s ta tio n s 0.5 Pa th lo s 1 m [d B ] Dis ta n c e d e p e n d e n t a tte n u a tio n fa c to r 3.5 Ce ll ra d iu s [m ] 36 0 Ch a n n e l b a n d wid th [MH z ] 5 Th e rm a l n o is e fl o o r [d B m ] # Re c e ivin g a n te n n a s 2,4 Ave ra g e te rm in a l p o we r [d B m ] 24 Ma x im u m te rm in a l p o we r [d B m ] 33 # Se c to rs p e r b a s e s ta tio n 3 Tra ffi c [u s e rs /k m 2 ] 1,3,1 0 In te rs ite d is ta n c e [m ] Sinc e th e p ath lo s s is rec ip ro c al th e algo rith m d es c rib ed b y E q. (1 1 ) c an b e im p lem ented in a d is trib u ted fas h io n w h ere th e term inals, s u b s eq u ent to c o nd u c ting th e nec es s ary m eas u rem ents s elec t th e ap p ro p riate b as e s tatio n. N o tic e th at th is fac ilitates a s o -c alled m o b ile c o ntro lled h and o ff, w h ic h p revio u s ly h as b een id entifi ed as a d es irab le p ro p erty in m u ltio p erato r enviro nm ents s inc e it am o ngs t o th er red u c es th e s ignaling b etw een th e invo lved o p erato rs [7 ] [1 2 ]. As w e neglec t effec ts o f m easu rem ent erro rs, w e im p lic ity assu m e th at th e term inals h ave th e ab ility to m eas u re th e p ath gain id eally fo r th e entire c and id ate s et o f b as e s tatio ns. G enerally h and o ver algo rith m s als o inc o rp o rating lo ad info rm atio n o u tp erfo rm th o s e th at m erely b as e th eir d ec is io n o n rec eived s ignal q u ality [6 ]. Th is is h o w ever d u e to lim ited s p ac e o u ts id e th e s c o p e o f th is p ap er. III. PERFORMANCE MEASURES AND SIMULATION MODEL Th is s ec tio n b riefl y d es c rib es th e s im u latio n enviro nm ent u s ed to q u antify ing th e gains in u s er th ro u gh p u t as s o c iated w ith natio nal ro am ing. A. Pe r fo r m a n c e M e a s u re s O ne ru d im entary m easu re o f th e b enefi t w ith natio nal ro am - ing is th e ac h ievab le u s er th ro u gh p u t. In th e p ap er w e u s e th e average u s er th ro u gh p u t, w h ic h als o c o rres p o nd s th e s y s tem th ro u gh p u t, to m eas u re gain an u s er ac h ieves in average. As no m inim u m gu aranteed d ata rates h ave b een as s u m ed (and c o ns eq u ently, no ad m is s io n c o ntro l h ave u s ed u s ed ) w e u s e th e average d ata th ro u gh p u t fo r th e 1 0 th u s er p erc entile, w h ic h d es c rib es th e s itu atio n fo r u s ers lo c ated at th e c ell b o rd er. B. S im u la tio n M o d e l Th ro u gh o u t th e s im u latio ns w e as s u m e th at th ree o p erato rs c o ex is ts. Po tential b o rd er effec ts are m itigated th ro u gh an im p lem ented w rap -aro u nd tec h niq u e and w ith in o u r s im u latio ns th e inter-o p erato r s ite d is tanc e d, lo ad ω and nu m b er o f rec eiving antennas M are varied. R em aining sim u latio ns p aram eters are su m m ariz ed in Tab le I.

138 122 Chapter 10. Throughput with National Roaming (Paper 6) Gain in 10th percentile user throughput with national roaming [%] Two receiving antennas and round robin scheduling 1 user/cell 3 users/cell 10 users/cell Inter operator site distance 78m Inter operator site distance 78m Inter operator site distance m Four receiving antennas and proportional fair scheduling 1 user/cell 3 users/cell 10 users/cell Inter operator site distance m Inter operator site distance 78m Inter operator site distance m (a) G ain in 1 0 -p erc en tile u s er th rou gh p u t (b ) G ain in average u s er th rou gh p u t F ig. 2. Th e relative gain in u s er th rou gh p u t for th e 1 0 th p erc en tile an d average u s er th rou gh p u t w ith n ation al roam in g. Th ree load s ωj {1, 3, 10 } are p res en ted for tw o variou s s y s tem s. Gain in average user throughput with national roaming [%] user/cell 3 users/cell 10 users/cell Two receiving antennas and round robin scheduling Four receiving antennas and proportional fair scheduling 1 user/cell 3 users/cell 10 users/cell Inter operator site distance 78m Inter operator site distance m IV. NUMERICAL RESULTS T h is sec tion starts with an gen eral evalu ation of th e th rou gh - p u t gain s a m ob ile op erator c an ex p ec t with n ation al roam in g. T h en, we an alyz e h ow th es e gain s d ep en d on th e m ic ros c op ic d ivers ity ord er an d th e in ter-op erator s ite d is tan c e of th e in - volved system s. Notic e th at eac h op erator op erator is restric ted to th eir own c arrier also in th e c ase of n ation al roam in g. A join t freq u en c y p lan wou ld in c reas e th e gain as s oc iated with n ation al roam in g even fu rth er. A. G a in s w ith N a tio n a l R o a m in g T h e u p p er p art of F igu re 2 p res en ts th e relative gain of th e average d ata th rou gh p u t for th e 1 0 th u s er p erc en tile as well as for th e average u ser in a system u tiliz in g two rec eivin g an ten n as an d a RR s c h ed u ler. T h ree load s ω j {1, 3, 10 } and two inter-op erator site distanc es are c onsidered. T h e relative gain for th e 1 0 th u ser p erc entile varies between % (1 0 0 % c orres p onds to a dou bling) and alth ou gh th e gain for th e average u ser sm aller it is still signifi c ant. T h e gains are m ainly m otivated by redu c ed p ath -los s, bu t als o aris e from lowered average interferenc e levels. From th e fi gu re we fu rth erm ore see th at th e gains inc rease with both interop erator s ite dis tanc e and load. T h e latter c an, at leas t p artially, be ex p lained by th at interferenc e levels at low loads already with ou t national roam ing is sm all. T h is m ak es th e interferenc e redu c tion enabled by national roam ing less p rom inent. For th e 1 0 th u s er p erc entile th e differenc e is ac c entu ated as th os e u s ers typ ic ally are loc ated in c ells with h igh loads. A t low loads, th is is c om p ensated by inc reased transm it p ower (see E q. 7 ), wh ic h redu c es th e relative interferenc e level even fu rth er. B. E ffe c t o f H ig h e r M ic ro s c o p ic D iv e r s ity T h e lower p art of Figu re 2 p res ents th e c orres p onding gains for a s ys tem u tiliz ing fou r rec eiving antennas and a P F s c h edu ler. C om p ared to th e th os e rep orted above, we s ee th at th e relative gains as s oc iated with national roam ing are redu c ed for m ore advanc ed s ys tem s. N otic e h owever th at c ons iderable gains s till ex is ts. T h e gain redu c tion aris es s inc e s ys tem s em - p loying op p ortu nis tic s c h edu ling, m ore rec eiving antennas or in any tec h niq u e im p roving th at ex p loits m ic rosc op ic diversity in order to im p rove th e link q u ality are assoc iated with h igh er S IR and, th u s, als o u s er th rou gh p u t levels. H enc e th e relative gain m ay dec rease even th ou gh th e absolu te one inc reases. T h is fac t is dep ic ted in Figu re 3, wh ic h p res ents th e data rate for th e 1 0 th u ser p erc entile. B esides th e two afore desc ribed s ys tem s, one u s ing fou r antennas and R R s c h edu ling and one u s ing two antennas and a P F s c h edu ler are treated. From Figu re 3 it is c lear th at u s er th rou gh p u t inc reas es wh en a P F s c h edu ler is u s ed ins tead of a R R s c h edu ler. A dditionally, it c an be s een th at th e relative gain as s oc iated with th e P F s c h edu ler inc reas es as th e load, and h enc e m u ltiu s er divers ity, inc reas es. In s ys tem s with h igh loads, e.g. ω j = 10 u s ers p er c ell, it yields gains sim ilar to th ose obtained with national roam ing. N otic e h owever th at a P F s c h edu ler gives s ignifi c ant gains als o for s m all loads. T h is, at fi rs t rath er u nex p ec ted res u lt, aris es s inc e th e nu m ber of u s ers in a c ertain c ell is P oisson distribu ted. T h u s th e worst u sers are, even at low loads, lik ely to be in c ells wh ere th e wireles s c h annel is s h ared with s everal oth ers s o th at a m u ltiu s er divers ity is p resent. Finally, we note th at adding m ore rec eiving antennas ou tp erform s P F s c h edu ling at low loads wh ereas th e gains c oinc ides at h igh er loads. C. E ffe c t o f In c re a s e d In te r-o p e ra to r S ite D is ta n c e Irresp ec tively of sc h edu ling strategy, nu m ber of rec eiving antennas and s ys tem load, th e u s er th rou gh p u t for th e 1 0 th u s er p erc entile initially inc reas es rap idly with d. H owever, at distanc es c om p arable to th e dec orrelation distanc e u ser

139 123 T h ro u g h p u t fo r th e 10th p e rc e n tile o f u s e rs [M b p s ] T w o a n te n n a s R R T w o a n te n n a s P F F o u r a n te n n a s R R F o u r a n te n n a s P F 1 u s e r/c e ll 3 u s e rs /c e ll 10 u s e rs /c e ll In te r o p e ra to r s ite d is ta n c e [m ] Fig. 3. Th e 1 0 -p erc en tile o f u ser th ro u gh p u t as a fu n c tio n o f in ter-o p erato r site d istan c e fo r d ifferen t an ten n as an d p ac ket sc h ed u lin g strategies. No te th at c o -sitin g (0 m site d istan c e) c o rresp o n d to th e sin gle-o p erato r c ase. throughput saturates. T his suggests that, for best effort traffi c an d a m in im um path los s han d off algorithm, on ly m in or c overage gain s are obtain ed by optim al c o-plan n in g of the n etwork s. Yet, it is of c ours e of prin c ipal im portan c e that the s ites are n ot c o-loc ated, in whic h c as e n o m ac ros c opic d ivers ity gain s are ac hieved. S im ilarly as for the 1 0 th user perc en tile the average user throughput in c reas es rapid ly with in c reas in g in ter-operator s ite d istan c e d. A roun d half of the gain is obtain ed alread y when s ites are s eparated with 7 8 m, jus t above the d ec orrelation d is tan c e of the s had ow fad in g. H en c e, a join t s ite plan n in g is n either m otivated from a sy stem c apac ity perspec tive. D ue to lim ited spac e these results are n ot in c lud ed in the paper. D. Va lid ity o f R e s u lts T he valid ity of our pres en ted res ults are n aturally s ubjec t to the applied sy stem m od ellin g an d assum ption s. For in s tan c e, in the s im ulation all operators were as s um ed to have the sam e ty pe of n etwork (base station d en sity, rad io ac c ess tec hn ology, etc.). M oreover we assum ed id eal path los s m eas urem en ts an d bes t effort traffi c with full buffers, whic h stron gly effec ts, for ex am ple, the m ultiuser d iversity gain. A ll of the aforem en tion ed as s um ption s are lik ely to y ield optim istic perform an c e gain s when m easured in absolute n um bers. H owever, if the s am e m od ellin g errors are m ad e in the sin gle operator referen c e c ase, the relative c om parison s should n ot be sign ifi c an tly affec ted. H en c e, even though the us er throughput of real n etwork s are un lik ely to be the s am e, it is reason able to believe that the relative gain s assoc iated with n ation al roam in g would be sim ilar as those presen ted here. V. CONCLUSIONS In this paper poten tial gain s as s oc iated with n a tio n a l ro a m - in g, where operators share the wireless in frastruc ture in ord er to in c reas e the c overage for higher d ata rates, have been s tud ied. T hroughout the s tud y ac tive us ers were as s um ed s tation ary an d c on n ec ted to the bas e s tation with highes t res ultin g path gain. E n ablin g n ation al roam in g c an in this sc en ario d ouble the d ata rates that us ers c los e to the c ell bord er ac hieves, even with n early c o-loc ated base station s. T he gain is partly d ue to red uc ed path loss, but also from a lower average in terferen c e level, whic h us ers c los e to the bas e s tation als o ben efi t of. E ven though the relative gain in c rease with in c reasin g in teroperator site d istan c e an d traffi c load, alm ost half of the gain arise from m ac rosc opic d iversity again st shad ow fad in g. T his suggests that the ben efi ts for operators to c o-plan their base station sites with n ation al roam in g are lim ited. M oreover we c om pared the gain s with n ation al roam in g to a few other (s tan d ard ) tec hn iq ues to in c reas e c apac ity an d c overage, suc h as m ulti-user d iversity sc hed ulers an d rec eive d iversity with m ultiple an ten n as. It was shown that the gain with n ation al roam in g is sign ifi c an tly higher, espec ially for low traffi c load, in whic h c ase the preferable ord er (without c on s id erin g c os ts or other ec on om ic variables ) is n ation al roam in g, m ore an ten n a elem en ts an d las tly m ore ad van c ed s c hed ulin g s trategies. N atural ex ten s ion s to this work would be to s tud y the effec ts of m ore s ophis tic ated han d off algorithm s, the in fl uen c e of in hom ogen eous us er d is tribution s, m eas urem en t errors, an d ad d ition al c apac ity gain s obtain able in s y s tem s with ad m is s ion c on trol (d ue to trun k in g effi c ien c y ). REFERENCES [1 ] C.M. Ch risten sen, Th e In n o vato r s D ilem m a, H arvard B u sin ess Sc h o o l Press, [2 ] J. H u ltell, K. J o h an sso n, an d J. M arken d ah l, B u sin ess m o d els an d reso u rc e m an agem en t fo r sh ared wireless n etwo rks, in Pro c. o f IE E E V TC -F a ll, Lo s A n geles, [3 ] P. Eard ley et al., M akin g M igratio n Easy: a key req u irem en t fo r system s b eyo n d 3 G, in Pro c. o f IE E E V TC -S p r ing, [4 ] No rth stream A B, 3 G ro llo u t statu s, Oc t , ISSN , PTSER :2 2, availab le at h ttp :// ts.se. [5 ] J. A. Village, K. P. Wo rrall an d D. I. Crawfo rd, 3 G Sh ared In frastru c - tu re, in Pro c.o f 3 G M o b ile C o m m u nic a tio n Te c h no lo g ie s, M ay [6 ] R. Verd o n e, A. Z an ella, Perfo rm an c e o f Rec eived Po wer an d Traffi c D riven H an d o ver A lgo rith m s in Urb an Cellu lar Netwo rks, IE E E Wire - le s s C o m m u nic a tio ns, Feb ru ary, [7 ] K. Pah lavan et al., H an d o ff in H yb rid M o b ile D ata Netwo rks, IE E E Pe r s o na l C o m m u nic a tio ns, A p ril, [8 ] A. Fu ru skär, Rad io Reso u rc e Sh arin g an d B earer Servic e A llo c atio n fo r M u lti-b earer Servic e, M u lti-a c c ess Wireless Netwo rks, D o c to ral D issertatio n, ISSN , M ay, [9 ] A. Tö lli, P. H akalin, H. H o lm a, Perfo rm an c e Evalu atio n o f Co m m o n Rad io Reso u rc e M an agem en t (CRRM ), in Pro c. o f IE E E IC C, [1 0 ] N. Nierb ert et al., A m b ien t Netwo rks: A n A rc h itec tu re fo r Co m m u n i- c atio n Netwo rks B eyo n d 3 G, IE E E Wire le s s C o m m u nic a tio ns, A p ril, [1 1 ] J. M c Nair an d F. Z h u, Vertic al H an d o ffs in Fo u rth G en eratio n M u lti- Netwo rk En viro n m en ts, IE E E Wire le s s C o m m u nic a tio n M a g a z ine, [1 2 ] T. B. Z ah ariad is et al., G lo b al Ro am in g in Nex t-g en eratio n Netwo rks, IE E E C o m m u nic a tio n M a g a z ine, Feb ru ary, [1 3 ] D igital m o b ile rad io to ward s fu tu re gen eratio n system, COST Fin al Rep o rt. [1 4 ] M. G u d m u n d so n, Co rrelatio n fo r Sh ad o w Fad in g in M o b ile Rad io System s, E le c tro nic L e tte r, Vo l. 2 9, [1 5 ] Un iversal Telec o m m u n ic atio n s System s (UM TS); Selec tio n p ro c ed u res fo r th e c h o ic e o f rad io tran sm issio n tec h n o lo gies o f th e UM TS (UM TS versio n ), Eu ro p ean Telec o m m u n ic atio n Stan d ard In stitu te.

140

141

142