Planning of UMTS Cellular Networks for Data Services Based on HSDPA

Transcription

1 Planning of UMTS Cellular Networks for Data Services Based on HSDPA Diana Ladeira, Pedro Costa, Luís M. Correia 1, Luís Santo 2 1 IST/IT Technical University of Lisbon, Lisbon, Portugal 2 Optimus, Lisbon, Portugal ladeira.d@gmail.com, pmbcosta@gmail.com, luis.correia@lx.it.pt, luis.santo@sonae.com Abstract This paper addresses the planning of UMTS cellular radio networks, for data services when implementing HSDPA, enabling the evaluation of the network performance in different scenarios. An algorithm was developed to allocate carriers, either exclusively for Release 99 or HSDPA, or to be shared between the two, taking multi-service users spatially distributed in a non-uniform way. A simulator was developed to implement the algorithm, corresponding to an average behaviour of the network. Several parameters are taken to evaluate network performance, like blocking and delay probabilities, as well as the average reduction in user bit rate. It is observed that the indoor penetration attenuation is a predominate factor in HSDPA, since when it increases from 11 to 25 db, the percentage of uncovered users can reach 51 %, for a radio network initially designed to provide outdoor coverage. Index Terms UMTS, HSDPA, Radio Network Planning, Multiple carriers. I. INTRODUCTION Data services are expected to have a significant growth over the next years, and will likely become the dominant source of 3G traffic and revenue [1]. This traffic growth should be supported through existing radio networks by means of using new techniques spectrally more efficient, e.g., HSDPA (High Speed Downlink Packet Access) in UMTS- FDD, i.e., Release 99. The introduction of this new technique is starting on sites of existing radio networks, where data traffic is higher, usually by sharing the transmitting power in single carrier Base Stations (BSs). Most of the operators are co-locating UMTS BSs with GSM ones, not only due to cost reduction but also because of the difficulties they face many times in getting new sites; therefore, an efficient use of the existing sites is of key importance. For an adequate expansion of the radio networks, it is crucial to evaluate the impact that the implementation of HSDPA has on network performance, namely by considering more than one carrier in a BS and power sharing, considering different types of traffic profiles and users distribution, and to analyse the performance of HSDPA in aggressive environments of data traffic. There are several possibilities for improving the performance of a radio network, e.g., by changing the tilt of the antennas [2], [3], or through the insertion of new BSs in key locations [4]. However, none of them analyses the specific case of the introduction of HSDPA, by taking its specific parameters into account, namely how many and what type of carriers shall be installed in each BS, as well as the distribution among the various carriers of the different types of users, with different services and QoS (Quality of Service) requirements. This paper addresses the evaluation of the radio network performance and the number and type of carriers needed by each BS, at a city level, for users having multi-service profiles and with realistic spatial distributions, considering different evolution scenarios, taking an already existing radio network with both Release 99 and HSDPA, which is the novelty of this approach. An algorithm has been developed, to calculate the number of carriers necessary in a BS, as well as if and how power should be shared (between Release 99 and HSDPA), for the traffic profile offered to the BS. A simulator has been developed, considering static users, based on [5], to implement the algorithm. The rest of the paper is organised as follows. In Section II, the basic structure of the developed algorithm and the performance parameters used for evaluation are described, as well as the main assumptions used in the development of the algorithm and in the simulations. Section III gives a brief presentation of the structure of the simulator. Simulation results are presented and analysed in Section IV. Finally, some conclusions are drawn in the Section V. A. Structure and Assumptions II. ALGORITHM The starting point of the algorithm is a given radio network deployment, with each BS having a tri-sector configuration. Furthermore, it is assumed that Release 99 is already fully deployed in the network, and that no further BSs will be added. The goal of the algorithm is to determine the number of carriers that each sector needs, for a given traffic and user distribution within its cell area, by analysing the coverage and capacity requirements imposed by users. Users are assumed to have various traffic profiles, with a multi-service perspective, ranging from voice (in Circuit Switch (CS)) to data at various bit rates (obviously, in Packet Switch (PS)); furthermore, users are considered to be nonuniformly distributed in the service area, approximating

2 realistic distributions as much as possible. For Release 99, coverage and capacity analysis is well defined by a link budget [6], which can estimate the total BS power requested by users, but for HSDPA [7] this approach is no longer possible, since the channel is shared and the signal is adjusted to instantaneous channel conditions, which means that one cannot have a specific value for the well-known E b /N parameter for each service, and a different approach for the SNR (Signal to Noise Ratio) value must be used. In the implementation of HSDPA, one can have two types of carriers, either sharing power between HSDPA and Release 99 users, or exclusively serving users connected to one of these versions. In the case of shared power, HSDPA users can either be accommodated in a fixed percentage of the total BS power, or use the power remaining from Release 99 at that moment. In either case, one must take into consideration that a certain amount of power has to be served for signalling and control, and that HSDPA requires additional power for this purpose. In order to decide whether a service is using Release 99 or HSDPA, a bit rate threshold value has been taken, i.e., data services requesting a bit rate lower than the threshold are allocated to Release 99, whether those with higher or equal values are served by HSDPA. The algorithm consists of two major blocks, Fig. 1: in the first one, a search is performed on the network, in order to find users with services for Release 99, so that the number of carriers required for a sector is calculated, and added to the basic network (the UMTS-FDD network with no carrier in use); in the second one, a search through all sectors is made, in order to find users with services for HSDPA, evaluating as well the number and type of carriers needed to be added, taking the available carriers and power left by the first dimensioning into consideration. The assignment of users in either of the two blocks only considers those that passed the coverage test, i.e., that can be in fact connected to the sector, taking the link budget and available capacity into account. The COST 231 Walfisch-Ikegami propagation model [8] was used, thus, assuming that all users are in an urban environment. Priority is given to Release 99 users over HSDPA ones, i.e., resources are initially allocated to the former, the latter being allocated in a second stage. Determines the Type of type of carrier for carrier the sector in evaluation analysis Release 99 Rel99 Dimensioning HSDPA Dimensioning Verifies Coverage the coverage calculation of the HSDPA users Fig. 1. Basic structure of the algorithm. Determines Number of the number of carriers carriers HSDPA the sector evaluation needs For Release 99 dimensioning, the conditions to be observed so that a new carrier is added to the sector are: 1. the number of requested codes exceeds the number of available ones in the carrier; 2. the UL (uplink) load factor exceeds a given threshold (usually taken as.5); 3. the DL (downlink) load factor exceeds a given threshold (usually taken as.7); 4. the power requested by users exceeds the available one. In the HSDPA dimensioning, after determining the type of carrier that can be added to the sector, resulting from the previous dimensioning, a new carrier is added if conditions 1, 2 or 4 are met. One should note that the DL load factor is not considered because the DL channel is shared among all users, hence, the approach of the load factor not being valid. If the condition for adding another HSDPA carrier to the sector is the outage of power, the algorithm only adds the new carrier after reducing the bit rate of all users to a given lower threshold, i.e., before a new carrier is added, the QoS provided to HSDPA users is reduced, by lowering the bit rate with which the best possible connection would be provided. Since this is not a real time simulation approach, in fact the bit rate with which users are connected to the network represents somehow the average value experienced by them during a given connection. This reduction is different from the one that is made in the allocation of carriers in Release 99, where the reduction only occurs when there are no more available carriers in the sector, and bit rate is lowered by steps, from 384 kbps to 128 kbps, and from this one to 64 kbps. In HSDPA, the reduction of bit rates is done in a continuous way, from its target service value until the decision threshold, below which, data services are allocated to Release 99 (approximation to Adaptive Modulation and Coding behaviour). As far as the SNR value is concerned, for link budget evaluation purposes, one has approximated it by the SINR (Signal to Interference and Noise Ratio) one. The curve of the bit rate, R b, as a function of the SINR presented in [6] was taken, and linearly approximated in the interval [.19, 3.] Mbps, which contains the range of bit rates for the services of interest in this work b [Mbps] (.95 SINR[dB] + log(.191)) R = 1. (1) By using this approximation, it is possible to determine the total BS power requested by users, but due to the assumptions underlying the curve, the following approximations were taken: the mobility of users does not affect the SINR value; each user is allocated only 1 code (SF16, i.e., with a spreading factor (SF) of 16) for data traffic; each user has always 15 codes (SF16) available, unless the carrier is shared with Release 99, a maximum of 5 codes (SF16) being considered in this case, for which the bit rate is approximated by 1/3. Additional details on the algorithm, and its implementation, can be found in [9].

3 B. Performance Parameters In order to evaluate network performance, the more or less usual set of parameters was taken in this work. Since the simulator upon which the algorithm was tested is a static one, each snapshot corresponding (formally) to one TTI (Transmission Time Interval), average values (and standard deviation ones) are taken for the performance parameters, after a series of runs are done for each scenario under analysis. Moreover, given this type of approach, time dependent parameters are not evaluated. The parameters used to characterise performance in Release 99 are: blocking probability, P b, for CS services, corresponding to calls that have no available channels when initiated, number of blocked calls P b = (2) total number of calls delay probability, P delay, for PS services, corresponding to users that are not served when they initiate a connection, number of delayed calls P delay = (3) total number of calls uncovered users, P unc, giving the percentage of users in the service area that are not covered by the network, under a given usage scenario, number of uncovered users P unc = (4) total number of users The parameters taken to characterise users and services in HSDPA are: delay probability, as in (3); uncovered users, as in (4); average reduction rate (ARR), which quantifies the reduction in bit rate that users suffer as a consequence of the lower QoS provided by the network in order to accommodate more users (a value of means that no reduction was performed), 1 ARR = 1 number of HSDPA users # HSDPA Rb_real i= 1 R b_nominal i where: o R b_real - user experienced bit rate; o R b_nominal - user service target bit rate; global transfer rate, showing the total throughput that is being handled by the sector, and/or the network, (5) Rglobal N users Rbj j= 1 = (6) where: R bj - user j experienced bit rate. III. SIMULATOR A simulator was used to implement the algorithm. Starting from an existing simulator [5], which implemented the basic features of UMTS-FDD, an extension was done to HSDPA, as well as the algorithm itself [9], Fig. 2. The basic blocks are User Generation and Network Dimensioning. 1 User generator (SIM) Network creation (UMTS_Simul) 2 Network dimensioning (Net_Opt) 1 Information regarding enviroment and users. 2 Network characteristics. 3 Performance parameters. Fig. 2. Simulator s structure. The users generator (SIM) is an independent program, developed in C++, responsible for the creation of users according to a certain traffic distribution. For each service in the set of available services, there is a Busy Hour Call Attempt (BHCA) grid, based on the ones coming from the MOMENTUM project [1] for the city of Lisbon. One can define the number of possible services, their characteristics and penetration rates, as well as the number of generated users and their spatial distribution in the service area. After this information is provided, users are randomly generated with a non-uniform distribution, taking the service area characteristics into account, being defined by their position, service, and type of mobility. This information is stored and used in the network dimensioning block. Network dimensioning is composed of 2 blocks, one based on a Geographical Information System (GIS) tool, and the other again based on C++. The former was developed in MapBasic for MapInfo [11], where the interface between the simulator s user and the simulator itself is established, and network settings are configured; a first evaluation of the network is performed, by considering the traffic generated by users, the configuration of the radio network, and so on. In the latter, network performance is evaluated, according to coverage and capacity analyses, by calculating performance parameters of the network, considering, among others, the BS antenna radiation pattern, and BS maximum power. The specific aspects of HSDPA, and the algorithm, were implemented in this block. The GIS module requires the information concerning users and the radio network: terrain geographical information, e.g., water or urban; users locations and characteristics; 3

4 location and configuration of the radio network, i.e., BSs, namely in terms of sectors; BS antennas radiation pattern; traffic distribution. The network settings of the simulator can be defined by the simulator s user, e.g., propagation model, type and number (up to 8) services. The parameters of the COST Walfisch-Ikegami propagation model are at the discretion of the simulator s user. The simulator verifies each user location, determining if each one is contained in the area of study (if not, they are excluded from the simulation). Then, it determines the BSs sectors (they are oriented at º, 12º and 24º from North) at which each user can be connected considering the best possible conditions, i.e., propagation, interference margins and service. The different users are represented in the output by a flag with a colour corresponding to the service at use, according to their position, as are BSs. The network analysis is performed by determining the number of carriers required by each sector, and the performance parameters. The information is then processed, and a statistical evaluation of the performance parameters is made in the sector domain. An experimental validation of the simulator would be desirable, but, unfortunately, this is still not possible, as there are not yet networks with 4 carriers including multiple single HSDPA ones. IV. RESULTS Scenarios taken for performance analysis of the algorithm are based on the usage of 8 services: Voice, Video Telephony, M-Entertainment, Messaging, Location Based Services (LBS), Multimedia Messaging Service (MMS), Internet Access, and Browsing. A set of 3 scenarios was defined in terms of traffic profile, considering different users penetration and target bit rates, i.e., Current, Data and Aggressive. The Current scenario is mostly composed of users with services using Release 99, the Data scenario increases the percentage of data services while maintaining the predominance of the services based on lower bit rates, and the Aggressive scenario increases data services penetration, their rates and services percentage, by giving a predominance to higher bit rates. Table I and Table II present the basic characteristics of these scenarios. Two values were considered in the simulations, for threshold between Release 99 and HSDPA, i.e., 384 and 512 kbps. A reference scenario, to be taken for comparison with others, was considered, being characterised by: the city of Lisbon as service area, Fig. 3; UMTS radio network co-located with a GSM one, Fig. 3; dynamic power management in the BS between Release 99 and HDSPA; up to 4 carriers available per sector; PS threshold between Release 99 and HSDPA of 384 kbps; penetration attenuation of 11dB for indoor scenarios; service penetration and service rates corresponding to the Current scenario; active set of 3; 1 35 users present in the service area. Table I - Services penetration. Service Service Penetration [%] Current Data Aggressive Voice CS Video Telephony CS M-Entertainment PS Messaging PS LBS PS MMS PS Internet Access PS Browsing PS Table II - Services rates and corresponding probabilities. Service Current Data Aggressive Rate[kbps] % Rate[kbps] % Rate[kbps] % Voice CS Video Telephony CS M PS Entertain Messaging PS LBS PS MMS PS Internet PS Access Browsing PS Fig. 3. Lisbon Map with BSs location and users distribution. In order to analyse network performance, several changes where made to the initial settings, namely, indoor penetration

5 attenuation, Release 99/HSDPA threshold, traffic distribution, and services rates. Since a statistical approach is taken with several random factors present in the simulator, 1 simulations were run for each situation under analysis (this number was considered to lead to adequate statistical relevance [9]), after which average and standard deviation values were calculated. By increasing the value of the indoor penetration attenuation, from 11 up to 25 db, Fig. 4, one can observe that while the probability of HSDPA uncovered users increases considerably, 25 to 51 %, as a consequence of the progressively more demanding scenario, the delay probability experienced by users on data services decreases, due to the lower number of users covered by the network. One should note that network coverage optimisation is not being considered here, therefore, there are users outside the range of a BS, due to the fact that BSs are co-located with a GSM network; if such an approach had been taken into consideration as well, the uncovered users probability would be very low, as a consequence of the increase of the number of BSs (which is kept constant at all times in this work). HSDPA Users [%] Delay probability Uncovered probability Indoor attenuation [db] Fig. 4. Evolution of HSDPA uncovered and delayed users with indoor penetration attenuation. Fig. 5 contains results when changing the Release 99/HSDPA threshold from 384 to 512 kbps. User Average Rate [%] HSDPA HSDPA Rel HSDPA Rel99 Rel HSDPA threshold value [kbps] Fig. 5. Average users rate characterisation for different Release 99/HSDPA thresholds. One observes that 384 kbps users have a significant decrease in their average service rate, around 2 %, and also that 128 kbps users are influenced by this change, their bit rate being slightly decreased. This is due to a more demanding Release 99 capacity, because of the increase of users and their service rates. Taking the differences between Release 99 and HSDPA users bit rates reduction processes into account, one can observe a significant increase on the need of carriers per sector, as consequence of the change in the Release 99/HSDPA threshold, Fig. 6. The main reason for this is the increasing demand for capacity from both Release 99, as previously referred, and HSDPA users (they cannot have their bit rate reduced, since it equals the Release 99/HSDPA threshold), and especially to the fact that Release 99 users have priority in the allocation of resources. When changing traffic distribution, services rates and corresponding distributions, one is changing the considered scenarios. Fig. 7 illustrates the total number of users served in the network, and its decreasing behaviour when more demanding scenarios are considered ( other rates being voice and other services below 128 kbps). The allowed rates are much higher in the Aggressive scenario than in any other; nevertheless, although there are fewer users in this scenario, they are being served at much higher rates, with a lower ARR, which translates into an increase of network capacity from implementing HSDPA. Carriers global distribution per sector[%] Number of users n=1 n=2 n=3 n= HSDPA threshold value [kbps] Fig. 6. Carriers global distribution per sector for different Release 99/HSDPA thresholds. other rates Current Data Agressive Scenarios Fig. 7. Served users distribution for different scenarios.

6 One should also note that as the scenario becomes more demanding, the number of required HSDPA carriers per sector increases. Also, the relative occupation of the carriers has very different profiles, when the scenario changes, Fig. 8. Since a total of 4 carriers is available, one never faces the situation where 4 carriers are allocated to HSDPA, but the Aggressive scenario is the only one in which some sectors do have 3 carriers allocated to HSDPA, implying a lower number of sectors with 2 carriers allocated to Release 99. Clearly, the traffic profiles offered by the service area to the radio network have an enormous influence on the allocation of carriers in the network, and its relative occupation between Release 99 and HSDPA, as well as shared ones. More results are available in [9]. Carriers distribution per sector [%] n=1 HS n=2 HS n=3 HS n=4 HS n=1 part. n=1 R99 n=2 R99 n=3 R99 n=4 R Current Data Agressive Scenario Fig. 8. Carrier distribution per sector for different scenarios. V. CONCLUSIONS This paper addresses the planning of a UMTS radio network, with Release 99 and HSDPA. An algorithm has been developed, to calculate the number of carriers necessary in each sector of a BS, as well as if and how power should be shared (between Release 99 and HSDPA). A simulator has been developed, considering static users, to implement the algorithm, allowing an evaluation of the impact of HSDPA in an existing network for different traffic scenarios. The simulator consists basically of two major blocks, i.e., user generator and network dimensioning. A total of 3 scenarios were created, as far as traffic generation is concerned, evolving from a lighter to a heavier data usage, i.e., Current, Data and Aggressive. Also, various situations were considered, for different indoor penetration attenuation, Release 99/HSDPA threshold, traffic distribution, and services rates, in order to evaluate their impact in the network performance. It is observed that indoor penetration attenuation restrains and limits the QoS desired by users with services using HSDPA, being necessary to have a good estimation of this value in order to avoid over or under-dimensioning network, since a value of attenuation between 11 and 25 db results in an average percentage of uncovered users between 25 and 51 %. It is clear that a UMTS network co-located with a GSM one is not sufficient to provide a good QoS for a specific traffic mix. Densification of BSs should be the next step for evaluating the HSDPA impact on a network. In terms of performance, when users with services at 384 kbps are considered, the use of Release 99 instead of HSDPA leads, on average, to a reduction around 2 % of the nominal rate, for the reference scenario. In the Aggressive scenario, with the threshold of 384 kbps, the results show that network capacity is improved due to the implementation of HSDPA, since there are more users using HSDPA (more users with higher bit rates), for a similar number of global carriers added to the network, than the ones in the Data scenario. In conclusion, as expected, the introduction of HSDPA improves network performance, but the adequate allocation of carriers should take the traffic profiles into consideration. REFERENCES [1] Qualcomm, HSDPA for improved Downlink Data Transfer, Report, Oct. 24 ( [2] A. Gerdenitsch, M. Toeltsch, S. Jakl and Y. Chong, A Rule Based Algorithm for Common Pilot Channel and Antenna Tilt Optimization in UMTS FDD Networks, ETRI Journal, Vol. 26, No. 5, Oct. 24, pp [3] I. Siomina, P-CPICH power and antenna tilt optimization in UMTS networks, in Proc. of AICT/SAPIR/ELETE 25 - Advanced Industrial Conf. on Telecomms./Service Assurance with Partial and Intermittent Resources Conf./ E-Learning on Telecomms. Workshop, Lisbon, Portugal, July 25. [4] A. Capone, E. Amaldi, F. Malucelli and F. Signori, Optimization Models and Algorithms for downlink UMTS radio planning, IEEE Wireless Communications and Networking, Vol. 2, No. 3, Mar. 23, pp [5] J. Cardeiro, Optimisation of Base Station Location in UMTS-FDD for Realistic Traffic Distribution, M.Sc. Thesis, Instituto Superior Técnico, Lisbon, Portugal, Mar. 26. [6] H. Holma and A. Toskala, WCDMA for UMTS, John Wiley & Sons, Chichester, UK, 24. [7] H. Holma and A. Toskala, HSDPA/HSUPA for UMTS, John Wiley & Sons, Chichester, UK, 26. [8] E. Damasso and L.M. Correia (eds.), Digital Mobile Radio Towards Future Generation - COST 231 Final Report, COST Office, Brussels, Belgium, 1999 ( [9] P. Costa and D. Ladeira, Optimum planning of UMTS radio networks for data services based on HSDPA (in Portuguese), Graduation Thesis, Instituto Superior Técnico, Lisbon, Portugal, June 26. [1] IST-MOMENTUM (Models and Simulations for Network planning and Control of UMTS), European Project, European Commission, Brussels, Belgium ( [11] MapInfo (