Nokia Siemens Networks Load balancing mobile broadband traffic in LTE HetNets The application of traffic steering methods
2/24 Table of contents 1. Executive summary... 3 2. Introduction... 4 2.1 Traffic Steering Policies... 4 2.2 Deployment Scenarios... 5 2.3 LTE Traffic Steering Methods... 7 2.3.1 Inter-frequency / RAT traffic steering... 7 2.3.2 LTE intra-frequency traffic steering... 9 3. Traffic Steering Performance... 10 3.1 Macro Scenarios... 10 3.2 Heterogeneous Network Scenario... 14 3.3 Performance improvements achievable by adding LTE intra-frequency Mobility Load Balancing... 17 3.4 Traffic Steering Strategy... 18 4. Traffic steering mechanisms between 3GPP and Wi-Fi... 19 5. Conclusions... 21
3/24 1. Executive summary Growing demand for mobile broadband services is encouraging the development of Heterogeneous Networks (HetNets). These include several 3GPP Radio Access Technologies (RATs) on multiple frequency bands, macro and small cells as well as Wi-Fi access points. GSM and EDGE will continue to be used for voice and machine type data services, while HSPA performance can rival that of LTE, typically using two carriers in the 2.1 GHz frequency band. LTE deployments, initially single carrier, will soon include multiple carriers and frequency bands, using any suitable chunk of new or refarmed spectrum. Several policies for steering traffic between access technologies have been developed, mostly based on load or service. Operators also increasingly base traffic steering on factors such as energy saving or different treatment of users and applications. While all the policies are executed in the Radio Access Network (RAN), those related to users and applications also need to be provisioned in the core network. Monitoring and optimization of traffic steering are also essential to ensure all components work together effectively. Traffic steering is a very broad area. This paper focuses on guidelines for operating e-utran Load Balancing for mobile broadband data traffic using traffic steering methods available in 3GPP Releases 9 and 10. In addition to LTE intrafrequency mobility load balancing, the following inter-frequency / traffic steering methods are evaluated: Traffic steering in idle mode, using absolute cell reselection priorities included in system information (referred to as Broadcasted Priorities) Traffic steering at connection setup Traffic steering handovers in connected mode Traffic steering at connection release, configuring absolute priorities included in release messages (referred to as Dedicated Priorities) Broadcasted Priorities can direct LTE capable terminals to LTE in initial deployments. As LTE terminals become more common and the network grows, additional traffic steering methods will be needed to ensure good performance. In both macro only and Heterogeneous Networks, most of the throughput gains are achieved at reasonable costs (in terms of signaling overhead for example) by using Broadcasted Priorities in combination with traffic steering at connection setup. Traffic steering in the connected mode maximizes gains by offloading users that rarely go to idle mode. While Dedicated Priorities do not provide additional throughput gains, they reduce costs by lowering the amount of idle mode interfrequency measurements (affecting device battery life) and signaling overhead in denser deployments (i.e. with smaller Inter-Site Distances).
4/24 In HetNet scenarios, adding LTE intra-frequency Mobility Load Balancing enables better distribution of load between macro and small cells by extending the small cells range. However, performance gains are small and highly dependent on users locations. Full gains can only be achieved when inter-cell interference coordination (macro cell blanking) is also used. The white paper briefly explains methods of implementing 3GPP-Wi-Fi interworking, namely Nokia Siemens Networks unique network based traffic steering, Access Network Discovery and Selection Function (ANDSF) and Hotspot 2.0 Access Network Query Protocol (ANQP). Traffic steering maximizes operators return on investment in network assets and spectrum by enabling all RATs and layers to be viewed as a logically unified network and according to operators preferred policies. 2. Introduction Operators have introduced more sites, more spectrum and higher spectral efficiency RATs to their networks in an attempt to meet traffic demand and improve users experiences, while keeping costs down. Achieving these aims means operators need to define policies on how and when their valuable network resources are used. Implementing these policies means enhancing the traffic steering logic in the RAN, which uses existing procedures to guide cell reselections and control connected mode mobility. It also requires new mechanisms to influence RAN decisions, based on the knowledge about subscribers and applications. Inter-working procedures to improve traffic steering between cellular and Wi-Fi networks also need enhancements. This paper focuses on the performance of traffic steering methods in LTE when used to balance cells load, based on the fact that LTE will be the technology with the greatest number of layers. The logic used for load balancing can also be reused and extended to implement other policies. Even though traffic steering looks the same for LTE and WCDMA/HSPA, several aspects are technology specific, for example, different inactivity handling in 3G due to the presence of cell-fach/pch. WCDMA/HSPA connected mode steering mechanism, i.e. Multi Band Load Balancing (MBLB), and steering of voice services are examined in two Nokia Siemens Networks White Papers Efficient resource utilization improves the customer experience and Voice over LTE, respectively (http://www.nsn.com/whitepapers). 2.1 Traffic Steering Policies Operators can choose to steer traffic in the RAN according to several policies. Cell load balancing. Load Balancing can be applied across layers in a given RAT, for example between cells using different carriers or between macros and co-channel small cells, as well as across different RATs. Balancing is triggered
5/24 when the load in a cell goes above a threshold and users are offloaded to less busy cells. When a cell is not over- loaded, other layers are not used, even when these are empty. This aims to improve performance for unsatisfied users (according to QoS requirements) while limiting the increase in signaling overhead and possibly device battery consumption. Note that cell load is defined as the sum of the resources used by satisfied GBR users (i.e. meeting the CBR target), the effective amount of resources used by unsatisfied GBR users and the amount of resources needed by ngbr users to meet a nominal bit rate. This paper covers both inter-frequency Load Balancing and LTE intrafrequency Mobility Load Balancing (MLB). Energy Saving. Offloading users to other carriers / RATs might be needed before a cell can be switched off to save energy. Differentiated treatment of services / applications. Operators may want to redirect specific service types (as defined by the Quality of Service (QoS) framework) or even specific applications to a given technology and/or layer to improve the end user experience. Differentiated treatment of user classes. Traffic steering can take into account specific priorities of user classes, for example according to information stored in the user profile in the Home Subscriber Server or on the measured user behavior. For instance, operators might decide to boost the quality of experience of business users and top revenue generators and penalize heavy data users. When executing a policy, the RAN needs to consider several factors: Network characteristics (for example feasible QoS); Performance requirements (for example radio and core network signaling overhead, call setup times); Device capabilities (such as radio access types and frequency bands supported) as well as battery consumption; Usage characteristics (mobility pattern, length of idle and connected periods). Cell load balancing, energy saving and differentiated treatment of services are policies enforced and executed in the RAN. On the other hand, policies related to user classes and applications are enforced in the CN and executed in the RAN. In fact, effective treatment of users and applications requires real-time information on local load and radio conditions available in the RAN and uses, in addition to traffic steering, radio resource management algorithms such as admission control, congestion control and fast packet scheduling. 2.2 Deployment Scenarios Figure 1 shows the spectrum resources available to European operators as well as their current and typical future usage. The frequency bands used for LTE deployment are typically 800 MHz, 1.8 GHz and 2.6 GHz. In the near future, 700 MHz and 3.5 GHz might become available as well (not shown in the figure). HSPA will in most cases be deployed in the 2.1 GHz band, since not all operators have spectrum in the 900 MHz band.
6/24 Figure 1 Spectrum resources in Europe, current and typical future allocation A detailed view of network evolution is available in a Nokia Siemens Networks White Paper Deployment Strategies for Heterogeneous Networks (http://www.nsn.com/whitepapers). Figure 2 shows an example of how a cellular network, including Wi-Fi, could evolve to address the increasing broadband traffic demand; relevant intra and inter-frequency / RAT traffic steering mechanisms are also listed. Figure 2 Deployment scenario evolution and related Traffic Steering mechanisms Step 1 depicts current operator deployments, including LTE macros on a single carrier in the 800 MHz band, HSPA macros on two or more carriers in the 2.1 GHz band and in some cases femto cells to improve indoor coverage; and finally mostly third party supplied Wi-Fi. Step 2 shows how capacity needs up to 2015 can be met by enhanced macro cells. This is achieved using new LTE frequency bands in the range 700-2600 MHz, new transmitter and receiver capabilities (for example higher order MIMO)
7/24 and possibly high(er) order sectorization using 6 sectors or Active Antenna Systems. Operators with spectrum in the 900 MHz band can use it to extend HSPA coverage. In the next phase (Step 3), additional capacity and higher bit rates will be provided by adding more LTE carriers and bands and complementing macro coverage with small cells. Even though not shown in Figure 2, GERAN coexists with the other technologies; while it is not well suited for broadband data, it is used for machine-to-machine communication and voice services. As an example, steering of voice from LTE to GSM can be achieved by Circuit Switched Fall Back (CSFB) or Single Radio Voice Call Continuity (SRVCC). Traffic steering in WCDMA / HSPA relies mainly on cell reselections based on Hierarchical Cell Structure or Absolute Priorities, redirections at connection setup and release and handovers in connected mode. Traffic steering in LTE needs to evolve from a simple approach, which keeps all capable devices in LTE whenever coverage is available (Step 1), to smarter strategies encompassing inter-frequency / RAT steering based on different policies (vertical arrows) and intra-frequency load balancing (horizontal arrows). Traffic steering between 3GPP RATs and approved Wi-Fi access uses device and Core Network based mechanisms (see Chapter 4). Proper configuration and alignment of the steering mechanisms is essential to support operators traffic steering policies. The next section explains the different steering methods from an LTE viewpoint, excluding voice specific mechanisms such as CSFB and SRVCC. 2.3 LTE Traffic Steering Methods 2.3.1 Inter-frequency / RAT traffic steering Figure 3 shows the traffic steering methods available in LTE. Figure 3 Traffic Steering methods
8/24 Traffic steering in idle mode applies to all users and used alone does not support any of the steering policies mentioned above. Traffic steering at connection setup can be triggered by load, user class or service. Steering in connected mode can be triggered by service (at bearer setup or reconfiguration), load and application class (when some indication is received from the CN). Finally, traffic steering at connection release can be used to try and balance the load or to support policies based on user class (for example in network sharing scenarios). 2.3.1.1 Traffic steering in idle mode During idle mode, the network can influence which RAT or layer the device will camp on by setting the values of Broadcasted absolute cell reselection Priorities (BP) from 0 (lowest) to 7 (highest priority). Being part of system information, these priorities apply to all users. When the highest priority access is not available at the device location, inter-frequency/rat measurements are performed periodically, affecting device battery life. 2.3.1.2 Traffic steering at connection setup A device changing from idle to connected mode is a candidate for steering. Once its radio capabilities are known and the initial context is set up (so that the Subscriber Profile ID for RAT/Frequency Priority Service (SPID) is available at the enb), the device can be instructed to perform inter-frequency/rat measurement (setting A4/B1 event) if its own cell load is too high or based on the user class. Based on the measurement results, the algorithm identifies suitable cells for offloading and selects the target for handover according to some criterion, such as best received signal, highest available capacity or randomly. The handover is performed after the bearer is established, so that the setup time is not affected. Retrieval of other cells load (if not belonging to the same enb), uses the Resource Status Reporting procedure for intra-lte neighbors and RAN Information Management (RIM) procedures on S1 interface for inter-rat neighbors. The load that can be accepted by an LTE cell, based on available hardware, transport, UL and DL radio resources for both data transfer as well as control signaling, is expressed as Composite Available Capacity (CAC). Alternatively, the steering logic can infer whether or not the target cell can accept load from how it responds to a handover request. Steering at connection setup requires inter-frequency/rat handovers that increase RAN and, when performed across enbs, CN signaling overhead. 2.3.1.3 Traffic steering in connected mode Traffic steering handovers can be triggered by changes in the radio network load during the lifetime of a connection, by the setup / reconfiguration of a service or based on the application used. Selection of users and target cells is based on similar logic to that used in connection setup and typically considers the trigger, device measurements, other cells load and user class. It might be argued that native mobility based on A2/A3 events and Reference Signal Received Quality (RSRQ) is enough to achieve load balancing in connected mode. Indeed, if no other traffic steering method is used apart from BP, RSRQ mobility can be used to an extent to balance load among layers in
9/24 denser deployments (smaller ISDs). However, higher throughput gains are consistently achieved with TS@CS in all scenarios. RSRQ depends on resource use only for good values of Signal to Interference plus Noise Ratio (SINR) and resource use does not reflect load since it can easily reach 100% when a cell serves a single best effort user. RSRQ mobility as a method of traffic steering is not discussed further here and a detailed study on RSRQ load balancing performance is available on demand. 2.3.1.4 Traffic steering at connection release Absolute Priorities used in idle mode can also be included in RRC connection release messages; since values can differ for each user, these priorities are commonly known as Dedicated Priorities (DP). DPs allow devices to be directed to the best candidate layer (according to some criterion, see below) at the same time as the state transition, overruling the broadcasted information. The prioritization provided at connection release is only valid for a limited time and as such is not effective for devices staying in idle mode for long. Approaches to defining the best layer vary from allocating DP randomly to enforcing changes only when the cell load is high and other layers received with good RSRP have sufficient capacity. Random allocation of priorities and long idle times offer lower performance, while short idle periods, use of radio and load conditions correspond to higher performance (see results in Sections 3.1 and 3.2). 2.3.2 LTE intra-frequency traffic steering Mobility load balancing is an intra-frequency SON mechanism that improves network performance by balancing the load of neighboring cells. This is achieved by decreasing the coverage of the high loaded cell while increasing the coverage (extending the range) of the underloaded cell(s) in a coordinated fashion by modifying the Cell Individual handover Offsets (CIOs). enbs rely on Resource Status Reporting procedure for exchanging load information (CAC) and negotiate the CIO changes using the Mobility Setting Change procedure. MLB can be applied between macros and between macros and small cells. However, it is more relevant in the case of hot spots, which are typically served by small cells, since there are more users suitable for offloading. Small changes in CIO do not always allow moving enough devices between the involved cells, limiting the potential for offloading. On the other hand, large changes can increase the occurrence of Radio Link Failures (RLF) due to the poor channel quality experienced by users in the range extension area. Thus, the maximum CIO value MLB is allowed to use needs to be adjusted by the Mobility Robustness Optimization (MRO) algorithm. Larger values of CIO can be used when the interference suffered by devices in the cell extension area can be mitigated by implementing blanking of selected macro cell radio frames (see next section).
10/24 2.3.2.1 Enhanced Inter-Cell Interference Coordination Enhanced Inter-Cell Interference Coordination (eicic) was initially specified for LTE in 3GPP Release 10 to maximize the gains from intra-frequency load balancing between macro and small cells. Harvesting the full gains of eicic requires device enhancements introduced in Release 11. eicic employs both CIO negotiation (as in MLB) and Almost Blank Subframes (ABS), in which the macro transmits only reference signals and some control channels but does not schedule devices. A small cell, informed about the ABS pattern through the Resource Status procedure, using the subframes with low interference, can serve users that are much further out than it could otherwise, giving significantly larger offload opportunities. Dynamic adaptation of CIO and ABS patterns allows an increase in both cell edge (top 5 th percentile) and average user throughput over all users in the dominance area of macro and the relevant small cells. 3. Traffic Steering Performance This chapter focuses on the evaluation of load balancing based on different combinations of steering methods described in Section 2.3.1 in two LTE dual carrier scenarios. Following the network deployment steps 2 and 3 described in Section 2.2, the first section presents simulation results of a macro only scenario, while Sections 3.1 and 3.2 analyze the inter-frequency steering algorithm behavior when small cells share one of the two macro carriers, without and with Mobility Load Balancing respectively. Finally, Section 3.4 summarizes the simulation results and provides recommendations on the usage of the different traffic steering methods. During the simulation campaign, different parameter settings for the traffic steering methods were tested. Performance was evaluated in terms of average and cell edge (top 5 th percentile) user throughput for Best Effort users and user satisfaction for Constant Bit Rate (CBR) users, considering load and device distributions for each layer and cell type. Costs were taken into account in terms of signaling load (measurement configuration, number of intra and inter-frequency handovers) and amount of inter-frequency measurement in idle mode and measurement gaps in connected mode. The following sections include only selected results for Best Effort services, since these predominate over CBR services. 3.1 Macro Scenarios Simulations for the macro-only scenario considered two deployments, one with an Inter-Site Distance (ISD) of 500m and the other with an ISD of 1732m. Each of the 19 sites included a three sectorized enb, a coverage layer at 800 MHz and a capacity layer at 2.6 GHz. Bandwidth on both carriers was 10 MHz.
11/24 Figure 4 Macro scenario, ISD 1732m; cell dominance at 800 MHz Figure 5 Macro scenario, ISD 1732m; cell dominance at 2.6 GHz While with smaller cells the coverage of the two layers is the same, in the 1732m ISD scenario the 800 MHz coverage is continuous (Figure 1) but the 2.6 GHz layer suffers from coverage holes (Figure 5). Uneven coverage causes some devices to perform inter-frequency mobility handovers and limits the steering possibilities in areas where only one layer is available, making the scenario worth considering. Users are uniformly distributed over the simulation area and initially all camp on the 800 MHz layer. They have a fixed amount of data to transmit and they move in random directions at 3 km/h. Broadcasted absolute Priorities (BP) set the higher frequency cell as the preferred layer. Figure 6 and Figure 7 show a comparison of average user throughput over all users, independent of the layer they are connected to. More precisely, the three groups of bars in both figures represent results for combinations of absolute priorities and other steering methods. Each group includes results for: Only absolute priorities (BP or BP + DP, red); Absolute priorities and steering at connection setup (TS@CS, purple); Absolute priorities and a combination of steering at connection setup and in connected mode (TS@CS + TS@CM, yellow). As mentioned in Section 2.3.1.4, DP performance is evaluated in worst and best case scenarios (lower and upper bounds). The combination of TS@CS and BP produces a consistent behavior in the two deployment scenarios and achieves most of the gain relative to the maximum average user throughput. Gains over absolute priorities only vary significantly in the two scenarios (13-222% on top of BP only). Adding TS@CM further improves performance (8-18%), steering users going less often to idle. TS@CM gains strongly depend on smartphone/dongle/tablet penetration and applications behavior, the configuration of inactivity timers and how often TS@CM tracks load. It is probable that in reality the relative gain compared to TS@CS will be higher.
12/24 Figure 6 Average user throughput over all users; ISD 500m Figure 7 Average user throughput over all users; ISD 1732m BP used alone causes congestion in the highest priority layer (at 2.6 GHz in the example) when this is available at all locations (ISD 500m). The poor performance of users camped on the capacity layer results in low average
13/24 user throughput over all users. In the 1732m ISD scenarios, a fraction of the devices performs mobility handovers in areas where the capacity layer has no coverage (Figure 9); in the coverage layer, users experience high bit rates, so that the average user throughput across all users is much higher even though the throughput distribution is highly imbalanced. Adding DP to BP, in the best case nicely balances the load, resulting in excellent average user throughput (BP+DP upper bound results for both scenarios). When priorities at connection release are set randomly, using DP results in poor performance (BP+DP lower bound results). Adding DP to TS@CS and BP does always improve the values of average throughput. On the other hand, using DP lowers the number of idle mode interfrequency measurements when a layer has coverage holes and may decrease the need to perform Load Balancing HandOvers (LB HO) for denser deployments (ISD 500m; Figure 8). In the scenario with larger cells (Figure 9), using DP keeps more users in the 800 MHz layer, slightly reducing the number of incoming mobility handovers but increasing the number of inter-frequency handovers in connected mode in areas where the 2.6 GHz layer is available. Figure 8 Amount of inter-frequency handovers per user per minute, ISD 500m
14/24 Figure 9 Amount of inter-frequency handovers per user per minute, ISD 1732m 3.2 Heterogeneous Network Scenario Figure 10 shows cell dominance in the simulated HetNet scenario. Figure 10 HetNet scenario, cell dominance
15/24 In addition to the macro coverage layer at 800 MHz and the macro capacity layer at 2.6 GHz (7 sites, 500m ISD), the scenario includes four small cells per macro cell deployed at 2.6 GHz. The bandwidth of the lower frequency is still 10 MHz, while that of the capacity layer is now doubled (20 MHz). Users are uniformly distributed over most of the simulation area but several hotspots are placed at the centre or edge of each macro cell. In order to reflect a more realistic deployment, small cells cover only 66% of the hotspot. Users have a fixed amount of data to transmit and they move randomly at 3 km/h; hotspot users movements are limited to the hotspot area. Broadcasted Priorities set the higher frequency as the preferred layer. Figure 11 depicts the average user throughput over all users. Figure 12 shows the load distribution for the different combinations of traffic steering methods. Note that only one set of results is shown for idle mode priorities (BP +DP), since there are no significant performance changes between lower and upper limits. The load is calculated as the sum of the Physical Resource Blocks (PRBs) allocated to unsatisfied users (i.e. below the target bit rate) plus the PRBs that satisfied users would use to reach the target bit rate. For each group of results, the three bars of the same color represents load in the macro at 800 MHz, macro at 2.6 GHz and small cells at 2.6 GHz, respectively. The average user throughput results show the same trend as in the macro only scenarios, with TS@CS added to BP achieving most of the gains (46%) and the most consistent behavior. The significant increase in absolute values is due to the increase in number of resources available at the 2.6 GHz layer. Figure 11 Average user throughput over all users
16/24 Figure 12 Load distribution per layer BP alone cannot achieve any balancing between the coverage and capacity layers; note that the 800 MHz macro layer is empty (there is no column) in the first group of red bars in Figure 12. Despite this fact, BP alone achieves better average user throughput over all users than in the macro case (Figure 11), due to the presence in the capacity layer of small cells. These cells serve most of the users but are still underloaded, given the amount of available resources. Activating DP allows the use of the macro layer at 800 MHz (BP + DP, first red column in Figure 12) and the attainment of a higher average throughput. Adding TS@CS to BP moves some users from the macro 2.6 GHz to the previously underused macro at 800 MHz; these users experience good throughputs. The lower load level on the macro at 2.6 GHz means a reduction of interference to the small cell users, which boosts their throughput and improves the overall performance. Other steering methods, TS@CM or DP, used in addition to BP and TS@CS, do not significantly improve load balancing between the two macro layers, nor the average user throughput. Given the users spatial distribution and mobility pattern used in the simulations, small cells load is at maximum 38% and is relatively stable irrespective of the traffic steering method used. Pushing all idle mode users to 2.6 GHz using Broadcasted Priorities ensures that small cells serve all users in their coverage area. Other steering methods, by offloading some of the users from the macro at 2.6 GHz to the macro at 800 MHz, also reduce the number of devices moving from macro to small cells in the capacity layer due to their normal mobility.
17/24 Thus, higher utilization of small cells is not possible in this scenario without changing their coverage area. This cannot be achieved with the inter-frequency steering methods examined till now and requires an intra-frequency load balancing method, called Mobility Load Balancing. 3.3 Performance improvements achievable by adding LTE intra-frequency Mobility Load Balancing While earlier the focus was on evaluating which inter-frequency traffic steering strategy performs the best, this section focuses on the benefits of adding intra-frequency Mobility Load Balancing to inter-frequency Load Balancing in Connected Mode. The scenario is similar to the one analyzed in Section 3.2, in which small cells cover 66% of the hotspot users but with a higher carried load. In the simulations, CIO changes were restricted to a maximum of 3 db in order to limit the occurrence of RLFs. Figure 13 Average user throughput over all users Figure 13 shows that the increase in average user throughput over all users enabled by MLB is limited to 11% over the reference case, which is steering in connected mode. This is achieved by redistributing the load from macros to small cells in the 2.6 GHz layer (Figure 14), which results in improved throughput for users connected to the macro cells at the expense of a decrease experienced by small cells users. This is explained by the higher load carried by small cells and by the fact that users in the range extension area may experience poor channel quality.
18/24 Figure 14 Load distribution per layer More substantial performance improvements in offloading potential and user throughputs can only be achieved by applying interference coordination in addition to adjusting mobility parameters (see Section 2.3.2.1). 3.4 Traffic Steering Strategy In initial LTE deployments, including a single carrier, usage of Broadcasted Priorities is sufficient to guarantee that LTE capable terminals camp on LTE, whenever it is available. Seeing that the low LTE device penetration does not result in significant load, this is sufficient. Once the deployment scenario includes more LTE carriers as well as different cell types (HetNet scenarios), taking into consideration the results shown in the previous sections as well as other scenarios analyzed in the performance study, activating inter-frequency steering at connection setup and Broadcasted Priorities is recommended. This combination of traffic steering methods attains most of the achievable gains in terms of average user throughput over all users and its behavior is consistent in all scenarios (see Figure 6, Figure 7 and Figure 11). Traffic steering in connected mode can improve average user throughput by offering more opportunities to steer devices that seldom go to idle (as in the case of USB sticks and smartphones).
19/24 Broadcasted Priorities can cause an increase in inter-frequency measurements in idle mode, affecting device battery life. Traffic steering at connection setup and in connected mode obviously require inter-frequency measurements and handovers in connected mode (Figure 8 and Figure 9), affecting RAN and CN signaling overhead. Adding Dedicated Priorities to traffic steering at connection setup and Broadcasted Priorities does not always improve the values of average throughput but may decrease the load balancing costs in specific scenarios. More specifically, if the two layers have the same coverage (as in ISD of 500m), by directing users to the best layer at connection release, this method often avoids the need for steering at connection setup and in connected mode, resulting in fewer inter-frequency measurements and handovers. On the other hand, when one of the layers has coverage holes (as in ISD of 1732m) using Dedicated Priorities reduces the number of inter-frequency measurements in idle mode but may increase the signaling overhead in connected mode (as shown in Figure 9). When the inter-frequency mechanisms are not sufficient to decrease the load (or operators own one frequency band only) on the macro layer where small cells are deployed, it makes sense to activate intra-frequency Mobility Load Balancing. The capability of MLB to move users from macro to the small cell depends greatly on the location of users and their velocity. When enough slow moving users are located in the range extension area, the 5 th percentile and average user throughputs can be increased. Further improvements in terms of offloading potential (i.e. larger CIOs) and throughput require mitigating the macro interference on such users by enabling eicic. 4. Traffic steering mechanisms between 3GPP and Wi-Fi In order to meet the increasing demand for data services in areas where high data consumption is anticipated (for instance indoor, in sport arenas and stadiums), operators may wish to complement their mobile networks with Wi-Fi access. In making Wi-Fi an integral part of mobile broadband offering, network operators need more control over when, where and how Wi-Fi networks are used. For example, operators may want to offload devices only to approved Wi-Fi networks ensuring a good user experience, or they may want to use roaming partner Wi-Fi hotspots (introducing additional costs) to give backup capacity only during mass events only. Existing solutions for 3GPP-Wi-Fi traffic steering are based on 3GPP Access Network Discovery and Selection Function (ANDSF) and Hotspot 2.0 Access Network Query Protocol (ANQP). ANDSF can be used both for offloading users from cellular networks and onloading them back from Wi-Fi. Tighter and more efficient traffic steering methods between the two networks are currently under discussion by standardization bodies (Rel. 12 Study Item on WLAN/3GPP Radio Interworking, http://www.3gpp.org/ftp/specs/html-info/37834.htm). While waiting for a standardized solution, Nokia Siemens Networks offers unique network
20/24 based dynamic load balancing between cellular and Wi-Fi. This is achieved by integrating the Wi-Fi network and service management servers that are aware of Wi-Fi user experience with a cellular operations system which has access to cellular load levels (see Smart Wi-Fi solution). A future version of the paper will analyze interactions between the already described LTE traffic steering methods and the mechanisms defined as a result of the 3GPP Release 12 study item, taking into consideration different operators policies for Wi-Fi usage. The purpose of ANDSF is to assist devices in discovering non-3gpp access networks. ANDSF can be implemented in both the Device Management and in Policy Server (PCRF) and it is transparent to the RAN. Figure 15 Example of ANDSF and HotSpot 2.0 architecture Figure 15 shows a possible solution, where ANDSF is implemented in the Mobile Device Management framework (see Smart Wi-Fi, http://www.nokiasiemensnetworks.com/portfolio/products/small-cells/smart-wi-fi). Its benefits include improved Wi-Fi usability and Wi-Fi security (automatically knowing which networks are friendly) and assurance of automatic Wi-Fi network use according to the operator s business strategy. The ANDSF network selection policies can be downloaded to the device, for example when the operator has a new subscriber, the subscriber has a new device or the operator has added a new roaming partner. Additionally, the device is allowed to contact the ANDSF server to update the network selection policies; this is useful whenever a user enters an area without valid policies being available, as when traveling. The device considers the ANDSF policies when selecting the network to be used; for instance, an operator may wish that users launching a browser or