Investigations into the costs, coverage, capacity and margins of mobile networks in Norway

Transcription

1 . Report for the Norwegian Post and Telecommunications Authority Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 25 June 2012 Ref:

2 Contents 1 Executive summary Variation in network costs Network coverage Network capacity Margin analysis Conclusions 19 2 Introduction 23 3 Network costs Update of the demand module (M7) The cost of termination under different coverage scenarios Total mobile economic cost Conclusions 40 4 Network coverage Inputs Coverage calculations for the operators Conclusions 43 5 Network capacity Inputs Capacity calculations for the 3G networks Capacity calculations for the 2G networks Conclusions 54 6 Margin analysis Refinements to the margin model Calculation of average margins Conclusions 60 Annex A Annex B Confidential coverage calculations Sensitivity of the TMEC curve to data usage

3 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Confidentiality Notice: This document and the information contained herein are strictly private and confidential, and are solely for the use of the Norwegian Post and Telecommunications Authority (NPT). Copyright The information contained herein is the property of Analysys Mason Limited and is provided on condition that it will not be reproduced, copied, lent or disclosed, directly or indirectly, nor used for any purpose other than that for which it was specifically furnished. Analysys Mason Limited St Giles Court 24 Castle Street Cambridge CB3 0AJ UK Tel: +44 (0) Fax: +44 (0) Registered in England No

4 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 1 1 Executive summary Analysys Mason Limited ( Analysys Mason ) was requested by the Norwegian Post and Telecommunications Authority ( NPT ) to undertake a number of quantitative investigations into the properties of the mobile network operators present in Norway. The background to these investigations is an upcoming regulatory decision that NPT will make on the future pricing of wholesale mobile voice termination (usually referred to as Market 7 under the European Commission s Regulatory Framework). In September 2010, NPT released a pricing decision for Market 7 covering the pricing of wholesale mobile voice termination to the end of As part of this decision, all mobile network operators (MNOs) 2 and mobile virtual network operators (MVNOs), excluding Network Norway, were planned to have symmetric mobile termination rates (MTRs) by the start of However, in May 2011, 3 the Norwegian Ministry for Transport and Communications revised this decision, deciding that Tele2 and Network Norway should continue to have asymmetric MTRs until the end of 2012, and also increasing the level of asymmetry for these operators. These two MVNOs had set up a joint venture called Mobile Norway to deploy a third mobile network in the country. The Ministry also determined that the price regulation for Tele2 and Network Norway could be revised in the event that real traffic development or investment costs change substantially compared to the assumptions on which the Ministry s decision was based. In addition, it could also be changed as a result of consolidations or structural changes affecting the operators activities in the market. Tele2 and Network Norway requested continued asymmetry beyond the end of 2013 (the end of the current period of price regulation), on the basis that they will roll out coverage beyond the threshold of 75% pertaining to the original Ministry decision. Figure 1.1 below illustrates the glide paths proposed by NPT and the subsequent revisions by the Ministry in the years 2012 and In particular: the purple line indicates the path determined for operators except Network Norway by NPT the pink line indicates the path determined for Network Norway by NPT the light blue line indicates the revised path determined for Tele2 by the Ministry the dark blue line indicates the revised path determined for Network Norway by the Ministry &p_d_c=&p_d_v= &p_d_c=&p_d_v= In general our analysis excludes ICE, which operates in 450MHz spectrum and focuses on data services rather than voice.

5 MTR per minute (NOK) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Figure 1.1: Glide paths for MTRs of the major mobile service providers in Norway [Source: NPT, 2012] T2 - current NN - current NN - old TN/NC/T2-old/MVNOs An important subsequent development occurred during the second half of 2011, when Tele2 s parent company (Tele 2 Sverige AB) completed its acquisition of Network Norway. Therefore, although the two operators are regulated using different MTRs, they are now both owned by the same company. The objective of this report is to investigate issues related to further network roll-out for Tele2 and Network Norway. In particular, the study focuses on the extent of coverage to which the Mobile Norway infrastructure can efficiently be deployed, and the extent to which it will be more efficient for the existing networks of Telenor and NetCom to carry traffic outside the coverage of Mobile Norway s network, on the basis of national roaming. NPT has two existing models that are of relevance to this study. The first is NPT s long-run incremental cost model ( LRIC model ) of mobile networks, which the regulator uses as the basis for the pricing of wholesale mobile voice termination in Norway. The latest release, v7.1, was used for the price regulation issued in September This model calculates the economic costs of the mobile operations of Telenor and NetCom, as well as for a third operator in Norway. The second model ( margin model ) has been developed recently by NPT to carry out margin squeeze tests on the mobile market. We have used both as part of our study. Our investigations have covered the key technical characteristics of mobile network infrastructure, namely coverage of networks in Norway (i.e. what parts of Norway they can provide mobile services to) and their capacity (i.e. how much traffic they can support from subscribers within their coverage areas). We also consider the costs of a network using the LRIC model, and assess the margins available on mobile services using the margin model. We summarise the main findings of our report in the rest of this section, in a manner that is suitable for public release. Section 1.1 examines our updates to the demand module in NPT s v7.1 LRIC model in order to obtain the v7.1a model, and describe some scenarios related to population coverage that we have analysed.

6 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 3 Section 1.2 outlines the results of the calculations of area and population coverage that we have undertaken for the mobile networks operating in Norway. Section 1.3 describes our investigations into the existing network capacity within the mobile networks of Telenor and NetCom, from the perspective of both voice and data. Section 1.4 investigates how NPT s margin model can be used to examine the margins for an operator using a mix of national roaming and its own network to carry traffic. Section 1.5 presents our conclusions based on our findings from Sections Variation in network costs In this section, we: describe the updates made to the demand calculations in the v7.1a LRIC model present our calculation of how the cost of termination changes with different coverage inputs describe how the total economic costs within the model (across all three modelled networks) vary with the population coverage of the third operator Updates made to the demand module As a first step in our investigations, we have updated the v7.1 model to include the most recent market data available within NPT (for the years 2009 and 2010, and the first half of 2011). Additionally, some of the demand forecasts have been updated to bring them into line with the historical data for The most significant updates to the forecasts in the v7.1a LRIC model were as follows: The forecast population was changed to be consistent with the data from the Statistisk sentralbyrå 4 and, in particular, the population forecast used in NPT s fixed LRIC model. Historical data for demonstrated a sharp increase in total low-speed data megabytes (i.e. data carried as either GPRS or UMTS-R99 traffic) that was not accounted for in the v7.1 model. To address this, we increased the forecasts for low-speed data per subscriber per month. Historical data for also indicated a requirement to increase the forecast of high-speed data megabytes per subscriber per month in the v7.1a model i.e. data carried within the HSPA 7 overlays on the 3G network For the demand update, we have assumed that the data labelled Ordinære mobiltelefoniabonnement in NPT s market statistics is low-speed data. For the demand update, we have assumed that the data labelled Dedikerte abonnement for mobilt bredbånd in NPT s market statistics is high-speed data. High Speed Packet Access.

7 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Calculation of the cost of termination under different coverage scenarios Having updated the market data in the LRIC model, we have considered four population coverage scenarios in the context of the v7.1a model for the third operator. These were: Scenario 1: 71% population coverage, assuming a sub-national network with close to 100% coverage in the main six Fylker and at least 50% coverage in other Fylker using only 2100MHz frequencies. Scenario 2: 87% population coverage, assuming a sub-national network with close to 100% coverage in the main six Fylker and at least 75% coverage in other Fylker using only 2100MHz frequencies. Scenario 3: 95% population coverage, assuming a sub-national network with 3G infrastructure using only 2100MHz frequencies. Scenario 4: 99.99% population coverage, assuming a national network with 3G infrastructure using both 2100MHz and 900MHz frequencies. Scenarios 1 3 use only 2100MHz frequencies for 3G since the majority of the 3G area coverage for these scenarios is modelled lies within the six Fylker, where the modelled third operator is assumed to use its 900MHz frequencies for the purposes of its 2G network. Therefore, we assume that the 900MHz frequencies are not available for use in the 3G network in these scenarios. These three scenarios are extensions of the sub-national network encoded within the v7.1 LRIC model. In scenario 4, we extend the modelled 3G network to full national coverage in all Fylker (at which national roaming is no longer required). Because of this, we include the use of 900MHz frequencies in the rural 3G network, although these frequencies are still used in the third operator 2G network in the population centres of the main six Fylker. We have calculated the unit LRAIC (long run average incremental cost) of termination for each of these cases using the v7.1a LRIC model. We also calculate the marked-up LRAIC (LRAIC+++) 8 for each case. Although LRAIC is the measure that NPT is using for future regulation of mobile voice termination, we believe that the LRAIC+++ also has particular relevance, given that it captures the entire cost base of an operator s network, including common costs. There are differences in unit costs between the different scenarios. For example, Scenario 1 has a lower LRAIC+++ of termination in the long run than Scenario 4, but only a marginally lower LRAIC. This is because the coverage network is significantly larger in Scenario 4, and LRAIC does not include the costs arising from the assets in the coverage network, which are added as one of the mark-ups in the LRAIC+++. For each of the four scenarios, we have derived the LRAIC+++ for the third operator for three different services: off-net voice, off-net SMS and data megabytes. These unit costs are shown in Figure 1.2 below. In particular, it can be seen that the unit costs of all the services increase when 8 The three mark-ups specified by the +++ relate to network common costs, location updates and business overheads.

8 Cost of service (Nominal NOK) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 5 the population coverage increases from 87% to 95% and then further to 100%. In particular, higher unit costs of services occur above 95% population coverage for voice, and above 87% population coverage for data. Figure 1.2: Output costs of voice, SMS and data in 2012 for the four scenarios [Source: NPT v7.1a LRIC model, 2012] Population coverage Off-net voice Off-net SMS Data This is because the investment required to achieve higher levels of population coverage increases significantly from 75% onwards, but particularly above 95%. We have then refined these calculations further, by attempting to identify whether there is a level of coverage by the third operator which appears to be most efficient from a total cost perspective, in terms of supporting the forecast network traffic in the Norwegian market. We describe this refinement below Calculation of total mobile economic costs Network Norway is currently in a national roaming agreement with Telenor, whilst Tele2 has a national roaming agreement with NetCom. As a result, traffic for the subscriber bases of Tele2 and Network Norway outside of their own network coverage areas will be carried on the other two networks. The LRIC model calculates the economic costs of Telenor and NetCom, as well as for a third operator with particular coverage assumptions. Depending on the assumed level of coverage of the third operator, some of its retail market share of traffic will be offloaded onto the Telenor and NetCom networks as national roaming traffic. If this traffic cannot all be carried by just using existing capacity in the other two networks, then additional network costs would be induced in the other two networks. Therefore there is a potential trade-off in the mobile market between (a) a situation in which the third operator has a smaller network footprint (thus reducing its own network costs but potentially

9 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 6 increasing the costs of the other two networks), and (b) a situation in which the third operator rolls out a larger network (reducing costs for the other two networks but increasing its own). We have investigated this trade-off by considering a range of particular values of long-term coverage for the third operator (up to 100%), achieved using UMTS operating in both 900MHz and 2100MHz frequencies. The assumed GSM deployment of the third operator is left unchanged in this investigation. For each particular value of long-term coverage by the third operator, we have calculated the total economic costs across all three modelled operators. This total is referred to as total mobile economic cost or TMEC. We have calculated the economic costs of each of the three MNOs for (i.e. from the modelling perspective, in perpetuity), discounted to 2012, using the 10.7% real discount rate assumed in the LRIC model. This discount rate is a consistent measure of network cost of capital for the mobile industry as a whole. We have also calculated the number of NodeBs the third operator deploys to achieve each level of coverage, since these assets are a significant driver of total network cost. This allows a curve to be drawn of TMEC against third-operator population coverage. A minimum on this curve corresponds to a level of population coverage by the third operator at which all traffic in the market is conveyed by the three networks most efficiently. Technically, such a point is referred to as static productive efficiency. As part of this analysis, we have classified the Fylker in Norway into: Major Fylker, which contain six of the largest cities in Norway (Akershus, Hordaland, Oslo, Rogaland, Sor Trøndelag and Vestfold), which were identified in the work on the LRIC model in Other Fylker, which are the remaining Fylker. We have considered a scenario for third-operator population coverage where 2100MHz 3G coverage is assumed to be static in Major Fylker at 98% and static in Other Fylker once it has reached 40%, whilst total 3G coverage first increases to 98% in all Fylker and then grows to 100% in all Fylker. This additional coverage is achieved using 900MHz frequencies. These profiles for coverage in Major Fylker and Other Fylker are illustrated in Figure 1.3 and Figure 1.4 below respectively. 9 responses_v7%201_public_ pdf, Section 4.5.

10 Total Major Fylke coverage Total Major Fylke coverage Total Major Fylke coverage Total Other Fylke coverage Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 7 Figure 1.3: Assumptions for 3G population coverage in Major Fylker [Source: Analysys Mason, 2012] 100% 100% 90% 90% 80% 80% 70% 70% Figure 1.4: Assumptions for 3G population coverage in Other Fylker [Source: Analysys Mason, 2012] 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% 2100MHz coverage Incremental 900MHz coverage 2100MHz coverage Incremental 900MHz coverage We have examined three different assumptions for the treatment of national roaming traffic: Assumption 1: Telenor carries 0% of this traffic (i.e. NetCom carries 100%) Assumption 2: Telenor and NetCom each carry 50% of this traffic Assumption 3: Telenor carries 100%. Assumption 2 is what the v7.1 LRIC model implicitly assumed. In this case, traffic roams onto the networks of both Telenor and NetCom. Some (or perhaps even all) of this additional loading could potentially be accommodated by capacity that these two operators have already deployed. Assumptions 1 and 3 test more extreme cases, where all of the national roaming traffic is carried by one operator (either NetCom or Telenor), increasing the likelihood that additional capacity deployments will be required on the hosting operator s network. Under each assumption, the modelled third operator deploys a 3G network based on the two coverage profiles above. 2100MHz coverage is driven directly by the pink lines in Figure 1.3 and Figure 1.4, whilst incremental 900MHz coverage is deployed according to the blue lines in these two figures. The curves of the TMEC under the three assumptions are shown in Figure 1.5 below. For each assumption, the dotted line indicates the population coverage by the third operator at which the TMEC is minimised.

11 Total mobile economic cost (billions) Third operator Node Bs Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Figure 1.5: TMEC curves for three coverage profiles [Source: NPT v7.1a LRIC model, 2012] Total 3G population coverage 100% on Telenor 50% on Telenor 0% on Telenor In Figure 1.6 below, we illustrate the number of NodeBs deployed by the third operator in the cases shown above. For a given level of third-operator coverage, the number of NodeBs is the same regardless of whether 100%, 50% or 0% of national roaming traffic is carried on Telenor: therefore the one curve in Figure 1.6 applies to each of the three curves in Figure 1.5 above. The curve below correlates closely to the three plots above Figure 1.6: NodeBs deployed for 3G coverage profiles [Source: NPT v7.1a LRIC model, 2012] Total 3G population coverage As can be seen above, the TMEC is minimised when the third operator has a population coverage of between 85% and 90%. It is also notable that in all three cases the TMEC curves tend to plateau at around the minimum value of the TMEC, meaning that there is little change in TMEC as thirdoperator coverage varies. This is particularly true when at least 50% of traffic is being carried on Telenor s network. Figure 1.5 also shows the TMEC increases significantly when the third

12 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 9 operator exceeds 95% coverage, which suggests that high levels of coverage (nearly nation-wide) do not lead to static productive efficiency. 1.2 Network coverage Based on the base station location data provided to NPT by all the MNOs, we have estimated the level of coverage of each of these networks in Norway. The main inputs used in the coverage calculation were: the location of base stations in Norway, using operator data from year-end 2011 assumed coverage radii for the base stations, by technology, which we have calibrated using the actual coverage of the operators a geographic dataset of Norway split by grunnkrets, the areas used by the Statistisk sentralbyrå (SSB), with population and area for each area. Some base station sites contain installations for only one technology for one operator, some for multiple technologies for one operator, and some are co-used by multiple operators. In addition, we recognise that definitions of land area coverage in Norway can also include areas of sea between the islands and around the coast of Norway. This additional coverage should stop at the water ground line (also known as the base line ), which we have estimated. This has allowed us to calculate coverage of any given network technology, split by physical land coverage and additional coverage to the base line. We have calculated network coverage using two methods: Method A: this calculates coverage by Fylke, with all coverage by base stations captured Method B: this also calculates coverage by Fylke, but only includes coverage in a Fylke from base stations that lie within that Fylke. These methods identify the population and area that lies within the coverage shapes of the networks in Norway. Method B will give lower values of coverage by definition, and was used in the coverage calculations undertaken for the LRIC model in previous work. Using Method A, we have also calculated the level of overlap between the network coverage of Telenor, NetCom and Mobile Norway. These calculations indicate that Mobile Norway s current 3G coverage lies almost entirely within the coverage of both Telenor and NetCom, and therefore Mobile Norway so far does not appear to be seeking to cover Norwegian population that is not already covered by both the other operators. 1.3 Network capacity We have investigated the existing capacity within the mobile networks of Telenor and NetCom, from the perspective of both voice and data. As inputs for this modelling we used the following

13 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 10 information for every sector across all the 2G and 3G base stations deployed in the networks of Telenor and NetCom: number of 2G transceivers and 3G channel elements activated busy-hour Erlangs (BHE) of voice traffic busy-hour megabytes (BHMB) of data traffic installed HSDPA 10 speed in Mbit/s (3G-only). This data was associated with sector co-ordinate data available within NPT. Where loading information was not provided in busy-hour format, then the data was converted into busy-hour equivalent volumes using data from the operator s individual network inputs within NPT s LRIC model. 11 We observe that the request to NPT for additional subsidy of the Mobile Norway deployment via continued MTR asymmetry primarily relates to 3G capacity (i.e. additional NodeBs). We do not believe that Mobile Norway has plans for national 2G coverage in the short term. Also, as Tele2 has suggested that mobile broadband is a specific factor for rolling out additional coverage, 2G networks would not be in a position to contribute significantly to this market. Therefore we believe that the free capacity of the 2G networks in Norway is of limited interest in this investigation, since the larger capacity of the 3G networks is where the efficient roll-out question applies. Our capacity calculations for the 3G networks are outlined in the following. We note that, in Norway, it often appears common practice to deploy omni-sectored base stations primarily for coverage, and then to sectorise these base stations as they require more capacity. This is the network design approach captured within NPT s LRIC model. Therefore, the number of sectors deployed on a base station can be an indicator of the capacity required. We have analysed the NodeBs deployed by each operator, categorised by the number of channel elements (CEs) activated. Figure 1.7 below illustrates the average voice BHE per sector for each of three categories of NodeBs in one operator s 3G network. As can be seen below, the average BHE per sector is relatively similar for the three main categories of CE activation and sectorisation High Speed Downlink Packet Access. In particular we have used a 12% conversion factor to express the ratio of cell-by-cell daily traffic compared to busyhour traffic.

14 BHE per sector BH MB per sector Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 11 Figure 1.7: BHE per sector and average sectors per base station, for the three main categories of CE deployment in one operator s 3G network [Source: Analysys Mason, 2012] Sectors per BTS Figure 1.8 below illustrates a similar calculation of average data BHMB per sector for each of these categories of NodeBs in the operator s network. As can be seen, once again the average BHMB per sector is relatively similar for the three main categories of CE activation. Figure 1.8: BHMB per sector and average sectors per base station, for the three main categories of CE deployment in one operator s 3G network [Source: Analysys Mason, 2012] Sectors per BTS As shown in the example chart below (Figure 1.9), plotting the data loading (BHMB per sector) against the voice loading (BHE per sector) for each 3G base station in the existing networks results in a fan-like distribution.

15 BHMB per sector Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 12 Figure 1.9: 3G network loading of base stations for one operator [Source: Analysys Mason, 2012] Voice BHE per sector We have categorised operators NodeBs based on these two loading dimensions, in increments of 1 BHE and 100 BHMB. Since we have determined that the number of sectors is also a driver of 3G capacity, we have captured this as a third dimension, by classifying 1-sectored, 2-sectored and 3-sectored NodeBs separately. We have then estimated which classes of NodeBs are likely to have free capacity, and those which are unlikely to have capacity. Our estimations are based on a maximum BHE per sector and BHMB per sector. A NodeB is unlikely to have free capacity if the average BHE per sector is greater than 6 BHE and the average BHMB per sector is greater than 1200 megabytes, whilst it is likely to have free capacity if the average BHE per sector is less than 3 BHE and the average BHMB per sector is less than 600MB. An example of the classification for one operator s 1-sectored NodeBs is shown in Figure All the NodeBs inside the green box in the diagram are likely to have free capacity (as they have a loading of less than 3 BHE and less than 600 BHMB), whilst all NodeBs outside the red box are unlikely to have free capacity (as they have a loading of more than 6 BHE and more than 1200 BHMB). No conclusion is made regarding the free capacity of the remaining NodeBs, although there may be the possibility of free capacity at these installations. Telenor s and NetCom s NodeBs have both been analysed using the same assumptions.

16 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 13 Erlangs Megabytes Figure 1.10: Categorisation 12 of 1- sectored NodeBs by voice BHE loading and busy-hour megabyte loading for one operator [Source: Analysys Mason, 2012] Analysis of Telenor/NetCom NodeBs across the whole country We have then identified the NodeBs belonging to either Telenor or NetCom that are likely to have free capacity in the busy-hour, and those that are unlikely to have free capacity. Figure 1.11 below shows the proportion of installations (in terms of each operator s total NodeBs and total sectors) that are likely to have free capacity (or not) in the busy-hour. These figures are shown for six regions and for Norway as a whole. Figure 1.11: Proportion of Telenor/NetCom installations likely to have free capacity (or not) in terms of total numbers of NodeBs and sectors [Source: Analysys Mason, 2012] Region Proportion with free capacity Proportion with no free capacity NodeBs Sectors NodeBs Sectors East (Hedmark, Oppland) 42% 51% 31% 25% South-East (Akershus, Oslo, Ostfold and Vestfold) 38% 41% 29% 28% South (Aust-Agder, Buskerud, Telemark, Vest-Agder) 38% 40% 23% 22% West (Hordaland, Rogaland, Sogn Og Fjordane) 54% 57% 21% 20% North-West (More Og Romsdal, Sor-Trøndelag) 37% 40% 32% 29% North (Finnmark, Nord-Trøndelag, Nordland, Troms) 39% 42% 27% 24% Norway 41% 44% 28% 25% 12 Each blue cell represents a count of NodeBs: the darker the colour, the higher the count of NodeBs in that category of traffic loading. This count is therefore a grouping of the detailed fan-shaped distribution of site by traffic

17 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 14 Based on these calculations, we estimate that over 2000 NodeBs in Norway are likely to have free capacity, because their current network busy hour load is less than 3 BHE and less than 600 BHMB. In particular, we also observe that the NodeBs in Telenor s and NetCom s networks that are estimated as likely to have free capacity are distributed across the whole country. Analysis of Telenor/NetCom NodeBs outside Mobile Norway s coverage area We have also analysed the Telenor and NetCom NodeBs that are outside an estimation of the Mobile Norway 3G coverage area. Our estimation uses the Mobile Norway 3G coverage network used to derive the values in Section 1.2, but with the cell radii reduced by 50%. This value may appear somewhat aggressive, but is intended to have two purposes: (a) to approximate the effects of cell breathing due to the peak network traffic loading during the network busy hour, and more importantly (b) to ensure that Telenor and NetCom NodeBs at the fringes of Mobile Norway s coverage are still taken into account. Figure 1.12 below shows the proportion of Telenor and NetCom installations outside of Mobile Norway s 3G coverage area that are likely to have free capacity. As can be seen, in Norway as a whole there appear to be almost 1000 NodeBs outside of Mobile Norway s coverage area which are likely to have free capacity, since their current network busy hour load is less than 3 BHE and less than 600 BHMB. Moreover, these NodeBs are distributed across the whole country. Figure 1.12: Proportion of Telenor/NetCom installations estimated to have free capacity (or not) in terms of total numbers of NodeBs and sectors, and located outside Mobile Norway s 3G coverage area [Source: Analysys Mason, 2012] Region Proportion estimated with free capacity Proportion estimated with no free capacity NodeBs Sectors NodeBs Sectors East (Hedmark, Oppland) 50% 61% 24% 18% South-East (Akershus, Oslo, Ostfold and Vestfold) 37% 40% 30% 29% South (Aust-Agder, Buskerud, Telemark, Vest-Agder) 38% 41% 27% 25% West (Hordaland, Rogaland, Sogn Og Fjordane) 69% 75% 9% 7% North-West (More Og Romsdal, Sor-Trøndelag) 51% 56% 25% 22% North (Finnmark, Nord-Trøndelag, Nordland, Troms) 43% 46% 26% 24% Norway 46% 52% 24% 22% As can be seen from this analysis, we estimate that all regions contain a mix of sites that are likely have free capacity, sites likely to have no free capacity and sites that do not definitely fall into either category. However, closer examination of the data in Figure 1.11 and Figure 1.12 indicates that a greater proportion of installations likely to have free capacity are in the areas outside of Mobile Norway s coverage. This can be seen in the West region for example, where 54% of all NodeBs and 57% of all sectors are likely to have free capacity. However, when only those installations outside the Mobile Norway coverage area are considered, these proportions increase

18 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 15 to 69% and 75% respectively. This effect appears to occur in all regions apart from South-East Norway. 1.4 Margin analysis This section describes the analyses that we have undertaken on the potential margins for the third operator, using the margin model recently developed by Analysys Mason for NPT. We have made a number of refinements to the margin model, for example to allow it to calculate margins for years other than The margin model uses various outputs from the LRIC model, which we have derived using the v7.1a LRIC model Refinements to the margin model Two new capabilities have been added to the margin model for the purposes of this study. These are the ability to calculate margins for a future year in the model, and the ability to consider the margins for a mobile-broadband-only product. We describe the enhancements in more detail below. Margin calculations for a future year (2020) A series of new inputs have been added in order to calculate the margins of an operator in a chosen future year (rather than just the current year). These additional inputs are as follows: Modelled year: the future year for which outputs are calculated. Network coverage as a proportion of traffic: this directly changes the proportion of the third operator s traffic covered by national roaming, thereby affecting the blended unit costs for each type of traffic. Usage multipliers: these determine the mark-ups that should be applied to traffic usage in the current year in order to reflect higher traffic usage in future years. Year-on-year trends for retail prices: these determine the retail prices for future years based on real-terms adjustments to current prices. Termination rates: the average fixed and mobile termination rates assumed in future years in the model. Year-on-year trends for wholesale prices of voice/sms/data: these trends determine the wholesale prices for the future year based on real-terms adjustments to current prices. The margin model can currently calculate the margins for products in 2011 and 2020 (both expressed in real 2011 NOK currency). The inputs in the model that are revised for the 2020 calculation are summarised below in Figure 1.13, along with the original 2011 value. Inputs derived using the v7.1a LRIC model are highlighted in bold.

19 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 16 Figure 1.13: Summary of inputs revised for a calculation in 2020 [Source: Analysys Mason, 2012] Input description 2011 value 2020 value Network coverage as a proportion of traffic (Option 1) 53.30% 70.50% Network coverage as a proportion of traffic (Option 2) 58.40% 87.10% Network coverage as a proportion of traffic (Option 3) 59.90% 90.50% Network coverage as a proportion of traffic (Option 4) 62.40% 95.80% Network coverage as a proportion of traffic (Option 5) 62.10% 99.75% Voice usage per subscriber as a proportion of 2011 voice usage SMS usage per subscriber as a proportion of 2011 SMS usage Data usage per subscriber as a proportion of 2011 data usage Retail prices for monthly fees and out-of-bundle traffic as a proportion of 2011 price * Average mobile termination rate across all mobile voice termination NOK 0.37 NOK 0.15 Average fixed termination rate across all fixed voice termination NOK 0.05 NOK 0.05 Voice/SMS wholesale prices as a proportion of 2011 prices * Data wholesale prices as a proportion of 2011 prices * Unit network costs of voice, SMS and data Derived from LRIC model * These values are modelled based on a 2% year-on-year decline. In order to generate the data for the last entry, the LRIC model must be run in a particular configuration. To calculate the unit network costs in 2011 and 2020, the third-operator calculation is run using a weighted average cost of capital (WACC) of zero, and with the assumptions in the v7.1 LRIC model restored regarding 3G infill coverage and 3G 2100MHz+900MHz area coverage. 13 We then calculate the blended 2G/3G unit costs of on-net voice, off-net voice, on-net SMS, off-net SMS and high-speed data megabytes. These costs are expressed in real 2011 NOK and include mark-ups for network common costs, location updates and business overheads. For the three cases of population coverage over 90% in 2020 set out above in Figure 1.13, we modify the coverage of the national configuration in the LRIC model. For the two cases under 90% in 2020 in Figure 1.13, we modify the coverage of a sub-national configuration. Margin calculations for a mobile broadband-only subscription We have based our assumptions of subscribers and usage for 2011 and 2020 on those used for the v7.1a LRIC model. Our assumed average revenue per megabyte is calculated from NPT s market statistics, based on the market-wide revenue per megabyte averaged across first-half 2010, secondhalf 2010 and first-half 2011 data. These assumptions are summarised below in Figure Inputs derived from the LRIC model are highlighted in bold. When a subscriber is served using national roaming, the margin model purchases the total national roaming capacity according to Telenor s current standard wholesale prices offer, with the 13 This is to be consistent with the calculations used to derive the unit costs of network services in NPT s original margin calculation model.

20 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 17 wholesale price decline (as a proportion of 2011 prices) applied from Figure 1.13 above in order to derive an assumed wholesale cost in Figure 1.14: Summary of inputs used for the modelled mobile broadband-only product [Source: Analysys Mason, 2012] Input description 2011 value 2020 value Mobile broadband-only subscribers as a proportion of total 11.2% 17.8% Assumed total usage in megabytes per subscriber per month Assumed revenue per megabyte, excluding VAT (NOK) * Megabytes included in bundle Proportion of clients exceeding their subscription 0% 0% * Modelled based on a 2% year-on-year decline. These mobile-broadband-only assumptions effectively correspond to a product where the operator receives, on average, NOK100 per subscriber per month in 2020, and each subscriber consumes, on average, 1250 megabytes per month in total (across downlink and uplink). The margin model can test other retail data-only product structures. We note that such structures (e.g. with additional charges for out-of-bundle megabytes) can influence the resulting margins to quite a significant extent. However, we have limited our investigation to just one example for a simple projection of average retail revenue and average consumption. The wholesale costs of national roaming for this product are based on Telenor s current standard wholesale offer. With regard to expected future usage, we note that an average usage of 1250 megabytes per month in total is consistent with the assumed long-term forecast for high-speed data usage from the v7.1 (and v7.1a) LRIC model. We have also tested more aggressive assumptions for mobile-broadband-only usage (specifically, with the usage doubled), in order to establish the impact of such increased usage on overall margins Calculation of average margins In this section, we illustrate how we have used the refinements to the margin model described above to investigate overall average margins for earnings before interest and taxes (EBIT) in three different cases: Lower megabytes per subscriber, lower penetration : we assume that mobile-broadbandonly subscriptions are 11.2% of all subscriptions, and that the data usage per subscriber per month is 1250 megabytes. Lower megabytes per subscriber, higher penetration : mobile-broadband-only subscriptions are 17.8% of all subscriptions, and the data usage per subscriber per month is 1250 megabytes. This is equivalent to the 2020 usage in the v7.1a LRIC model.

21 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 18 Higher megabytes per subscriber, higher penetration : mobile-broadband-only subscriptions are 17.8% of all subscriptions, and the data usage per subscriber per month is 2500 megabytes. In each case, we calculate the average EBIT margin in 2020 under the five options for 2020 population coverage (ranging from 70.50% to 99.75%, set out in Figure 1.13 above). We calculate these values of EBIT for two situations: a basket of four handset user profiles (consisting of the four products originally considered with the margin model) and a basket of five products (i.e. including the mobile broadband-only product). Our calculations apply the national roaming prices incorporated within NPT s margin model. For an operator which serves its retail traffic using a mix of its own network (within its own coverage) and the national roaming agreement, it indicates the following: the change in EBIT margin on the four voice-focused products achieved by increased coverage beyond 75% population coverage is almost zero the change in EBIT margin on the five product basket does not increase beyond 75% population coverage, and declines slightly beyond 87% coverage. We do observe that the overall results are influenced by not only the level of traffic on owncoverage versus national roaming, but also the prices and pricing structure available for national roaming. The availability of alternative prices or an alternative pricing structure, either current or future, could lead to different EBIT results and our findings should be taken in that context. We observe that operators are currently upgrading the installed speeds in their networks 14, and secondly that they currently offer products with high allowances 15, meaning that the envisaged average usage could be assumed to be above even our higher megabytes per subscriber. However, the availability of such options certainly does not imply that subscribers will be mainly purchasing only this product and consuming the full allowance. In fact, average usage may be diluted by consumers buying the cheaper products with smaller allowances. It is unlikely that operators will be able to achieve more (or even the same) revenue per megabyte from higher usage in the future, meaning that the average revenue per megabyte is likely to be lower (as we have reflected in Figure 1.14 above). Therefore, higher usage may lead to lower revenue per megabyte, which would most likely result in a lower average EBIT across both voice-focused and data-only products. Therefore, we believe that our cases above are a reasonable and conservative view of the possible margins achievable For example, see a recent announcement by NetCom at /journal_content/56_instance_w4vy/10156/ For example, see where Telenor offer products with an allowance of up to 20GB.

22 Third operator Node Bs Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Conclusions This section presents our main conclusions from each of the areas of investigation described above Variation in network costs The TMEC calculations show that TMEC reaches a minimum level when the population coverage of the third operator lies between around 85% and 90%, although the curves are fairly shallow (particularly when at least 50% of the traffic offloaded by the third operator is carried on the Telenor network). This indicates that the 85% 90% range of population coverage is broadly efficient in terms of carrying the market traffic with the minimum TMEC. Further, the curves in all scenarios indicate that providing coverage to the last 5% of population (above 95%) increases the TMEC significantly. In addition to this analysis, we also observe that there appear to be benefits to deploying (urban) coverage to the Northern Fylker (Nord-Trøndelag, Nordland, Troms and Finnmark). This is due to the fact that the most urban parts of these Fylker can be covered with a very small number of base stations, particularly with 900MHz frequencies, as illustrated in Figure 1.15 below. The chart below illustrates that 12% of Norway s population lies within these Fylker, and almost 11% out of this 12% can be covered with approximately 50 NodeBs using 900MHz frequencies. These curves are calculated using the LRIC model (i.e. the number of NodeBs required for each Fylke is calculated separately and then added up) Figure 1.15: NodeBs required to cover the Northern Fylker (coverage specified is proportion of total Norwegian population) [Source: NPT v7.1a LRIC model, 2012] MHz 900MHz

23 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Network coverage As stated above, our coverage calculations indicate that Mobile Norway s current 3G coverage lies almost entirely within the coverage areas of both Telenor and NetCom. Therefore, Mobile Norway does not so far appear to be seeking to cover population that is not already covered by both the main operators Network capacity Our analysis indicates that, even under the conservative capacity assumption of 3 BHE of voice and 600 BHMB of data, there is still a significant number of base stations in the Telenor and NetCom networks that are likely to have free capacity. Moreover, in almost every region this proportion is higher for the Telenor and NetCom NodeBs lying outside the existing Mobile Norway coverage area. Our estimations of free capacity are conservative given that they are derived, on average, across all of the NodeBs in the operators networks: data submitted by the operators indicates that there are NodeBs in their networks that are able to support significantly higher traffic volumes than these assumed threshold capacities. In addition, the traffic loading which we have been provided with will already include some national roaming traffic from the subscriber bases of Tele2 and Network Norway (i.e. both sets of subscribers are already occupying capacity). Figure 1.11 and Figure 1.12 in particular indicate that there is likely to be free capacity in all regions of Norway, including the Northern Fylker where Mobile Norway s coverage is currently limited. We note that the network loading data from the operators is predominantly from the first three months of However, usage in Norway is subject to seasonal effects, particularly in those areas with an abundance of holiday homes such as southerly coastal areas (which have lower traffic in the winter months and higher traffic in the summer months). Given this strong seasonal effect, we suggest that, it is not necessarily clear that infrastructure duplication by a third operator in these areas is efficient. Figure 1.16 below illustrates the estimated loading on a base station in Oslo versus a base station in a holiday home area. The two will experience opposite seasonal effects, with a slight decline in traffic in Oslo during the holiday period, and an increase in traffic in the holiday home area. As a result, the Oslo base station has some additional unused capacity in the summer months, as it must accommodate the higher levels of traffic in the non-summer months. However, if the other base station is to be able to carry the peak traffic in the summer months, then this results in significant unused capacity in the nonsummer months. Hence, additional deployments in such areas would be poorly utilised for many months of the year (to a far greater extent than in Oslo or other urban areas).

24 loading loading Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 21 Figure 1.16: Illustration of difference in used and unused capacity due to seasonal effects in base stations in Oslo versus a holiday home area [Source: Analysys Mason, 2012] Loading over the year on a base station in Oslo Loading over the year on a base station near a holiday home area months months KEY Used capacity Unused capacity We presume that this is already the situation experienced by Telenor and NetCom, so we consider it debateable as to whether it is efficient to have potentially large seasonal infrastructure utilisation effects duplicated on three networks rather than two (or indeed four networks rather than three, if one also takes into account the widespread coverage of ICE in the rural areas of Norway). We also observe that the three networks in Norway are entirely standalone, with no active network-sharing agreements currently in existence (other than normal sharing on third-party locations). In a number of similar European countries (such as Sweden, Denmark and the UK), domestic mobile operators have increasingly been entering into (or, in the case of Sweden, have started with) active network-sharing agreements. We believe the benefits of this active sharing are more strongly felt in rural areas, where networks will be less capacity-constrained than in urban areas. Tele2 has access to direct expertise on this type of activity through Tele2 Sweden s joint venture with Telenor Sweden (Net4Mobility), and through Tele2 Sweden s joint venture with TeliaSonera Sweden (SUNAB) Margin analysis When we consider the average EBIT margins across the basket of five products in the margin model (having also included a mobile-broadband-only product), it can be see that the level of owncoverage versus national roaming has a relevant key role to play in determining the overall results. Between 70% and 87% coverage the average EBIT margin is fairly constant, but falls as the network progresses beyond 90% coverage towards national coverage Final comments We observe that the number of additional sites needed for the third operator to go from 75% coverage to 85% coverage is relatively small (less than 100 sites), since this can be achieved by deploying to the population centres in smaller towns and other Fylker currently not covered by Mobile Norway.

25 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 22 Several of our investigations (including work with NPT s existing models and in particular our curves for TMEC) indicate that a coverage level of 85% 90% results in a broad minimum of cost. In order to go from around 85% 90% population coverage up to 95% requires a few hundred additional sites, which cause increases in the average unit costs of traffic. Extending the network beyond 95% to near-national coverage requires significantly more than a thousand additional sites. In parallel to this, we estimate that there are many hundreds of sites that are likely to have existing spare capacity which could be utilised by national roaming subscribers. Unless the third operator ceases to use national roaming as it approaches national coverage, the average EBIT margin gets increasingly worse (assuming current national roaming prices). This means that at current national roaming prices, to gain the full benefits of national coverage from its own network, the third operator needs to push its coverage significantly beyond 90% coverage all the way to 99% or higher. There is evidence of increasing network infrastructure sharing in other nearby European nations such as UK, Sweden and Denmark, and this which would appear a relevant option in the Norwegian context. Given these final remarks we conclude that the efficient level of population coverage for the third operator network in Norway should be 85% 90%, but there is neither a strong efficiency gain nor evident business case for extending the third standalone network (i.e. unshared) coverage to reach complete national coverage (99% population or more).

26 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 23 2 Introduction Analysys Mason Limited ( Analysys Mason ) has been requested by the Norwegian Post and Telecommunications Authority ( NPT ) to undertake a number of quantitative investigations into the properties of the mobile network providers operating in Norway. The background to these investigations is an upcoming regulatory decision for NPT on the future pricing of wholesale mobile voice termination (usually referred to as Market 7 under the European Commission s Regulatory Framework). In September 2010, NPT released a pricing decision for Market 7 covering the pricing of wholesale mobile voice termination to the end of As part of this decision, all mobile network operators (MNOs) 17 and mobile virtual network operators (MVNOs), excluding Network Norway, had symmetric mobile termination rates (MTRs) by the start of In May 2011, 18 the Norwegian Ministry for Transport and Communications ( the Ministry ) revised this decision, with continued asymmetry for Tele2/Network Norway until the end of 2012, as well as increased levels of asymmetry for these operators. The Ministry also determined that the price regulation for Tele2 and Network Norway could be revised in the event that real traffic development or investment costs change substantially compared to the assumptions on which the Ministry s decision was based. In addition, it could also be changed as a result of consolidations or structural changes affecting the operator s activity in the market. Tele2 and Network Norway have requested continued asymmetry beyond the end of 2013 (the end of the current period of price regulation), on the basis that they would roll out coverage beyond the threshold of 75% pertaining to the original Ministry decision. Figure 2.1 below illustrates the glide paths proposed by NPT and the Ministry in the years 2012 and In particular: the purple line indicates the path determined for operators except Network Norway by NPT the pink line indicates the path determined for Network Norway by NPT the light blue line indicates the revised path determined for Tele2 by the Ministry the dark blue line indicates the revised path determined for Network Norway by the Ministry &p_d_c=&p_d_v= &p_d_c=&p_d_v= In general our analysis excludes ICE, which operates in 450MHz spectrum and focuses on data services rather than voice.

27 MTR per minute (NOK) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Figure 2.1: Glide paths for MTRs of the major mobile service providers in Norway [Source: NPT, 2012] T2 - current NN - current NN - old TN/NC/T2-old/MVNOs An important subsequent development occurred during the second-half of 2011, when Tele2 s parent company (Tele2 Sverige AB) completed its acquisition of Network Norway. Therefore, although the two operators are regulated using different MTRs, they are now both owned by the same company. The objective of this report is to investigate issues related to further network roll-out for Tele2 and Network Norway. In particular, the study focuses on the extent of coverage to which the Mobile Norway infrastructure can efficiently be deployed, and the extent to which it will be more efficient for the existing networks of Telenor and NetCom to carry traffic outside the coverage of Mobile Norway s network, on the basis of national roaming. NPT has two existing models that are of relevance to this study. The first is NPT s long-run incremental cost model ( LRIC model ) of mobile networks, which the regulator uses as the basis for the pricing of wholesale mobile voice termination in Norway. The latest release, v7.1, was used for the price regulation issued in September This model calculates the economic costs of the mobile operations of Telenor and NetCom, as well as for a third operator in Norway. The second model ( margin model ) has been developed recently by NPT to carry out margin squeeze tests on the mobile market. We have used both as part of our study. Our investigations have covered the key technical characteristics of mobile network infrastructure, namely the coverage of a network (i.e. what parts of Norway it can provide mobile services to) and the capacity of a network (i.e. how much traffic it can support from subscribers within its coverage). We also consider the network costs using the LRIC model, and assess the margins available on mobile services using the margin model. The remainder of this document presents the results of our investigations as follows:

28 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 25 Section 3 examines our updates to the demand module in the v7.1 LRIC model in order to derive the v7.1a model, and describe some scenarios related to population coverage that we have analysed. Section 4 outlines the calculations of area and population coverage that we have undertaken for the mobile networks operated in Norway, supplemented by Annex A Section 5 describes our investigations into the existing network capacity within the mobile networks of Telenor and NetCom, from the perspective of both voice and data Section 6 investigates how NPT s margin model can be used to examine the margins for an operator using a mix of national roaming and its own network to carry traffic. The report includes a number of annexes containing supplementary material: Annex A provides additional information and calculations regarding our coverage calculations summarised in Section 4 Annex B considers the effect of higher data forecasts on the TMEC curves. Certain sensitive data within this report has been redacted, as shown by the scissor symbol ( ).

29 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 26 3 Network costs As a first step in our investigations, we have updated the v7.1 model to include the most recent market data available within NPT (for the full years 2009 and 2010, and the first half of 2011). This chapter is laid out as follows: Section 3.1 outlines the updates made to the demand calculations Section 3.2 describes how the cost of termination changes with different coverage inputs Section 3.3 describes how the total economic costs within the model (across all three modelled networks) vary with the population coverage of the third operator Section 3.4 presents our conclusions. The objective of the third step (Section 3.3) is to determine whether there is a level of coverage for the third operator which is most efficient in terms of the total cost experienced by the whole market. 3.1 Update of the demand module (M7) Using the NPT market data for years 2009, 2010 and the first half of 2011, we assigned all mobile service providers to a host Telenor, NetCom or ICE. ICE is assumed to host only its own subscribers. The existing worksheet for the demand data (D3_M6) has been retained. A copy of this worksheet (D3_M7) has been updated in order to align demand inputs (mainly for Telenor and NetCom) with the NPT market data. The loading assumptions for the operator were retained from the v7.1 model. The following inputs were updated: Figure 3.1: Updates to the demand inputs in the D3_M7 worksheet [Source: NPT LRIC model, 2012] Input Operators updated Years updated Digital mobile penetration (year-end) Total mobile market Mobile broadband penetration (year-end) Total mobile market GSM market share by operator (year-end) Market share of high-speed data subscriptions by operator (year-end) Total outgoing voice minutes per (year-end) subscriber per month Base case for Telenor, Tele2, Network Norway, TDC, Ventelo Telenor, NetCom, Tele2, Network Norway, TDC, Ventelo, ICE Telenor, NetCom, Tele2, Network Norway, TDC, Ventelo On-net minute proportion Telenor, NetCom Incoming voice from fixed networks per subscriber per month Incoming voice from international per subscriber per month Telenor, NetCom Telenor, NetCom

30 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 27 Input Operators updated Years updated Incoming voice from other mobile networks per subscriber per month Incoming voice from other mobile networks per subscriber per month Telenor Telenor The Population-year end data was also updated for the years using the same forecasts as contained in NPT s LRIC model for fixed networks (v1.6). Some of the demand forecasts in the D3_M7 worksheet have been updated to bring them into line with the historical data included in the model for In particular, we have updated the forecasts for: Low-speed data from 2012 onwards i.e. data that is assumed to be carried as either GPRS traffic on the 2G network, or as R99 traffic on the 3G network digital mobile penetration (year-end) from 2012 onward incoming voice per subscriber per month for NetCom in 2012 high-speed data usage per subscriber per month for Telenor and NetCom in 2012 i.e. data that is assumed to be carried using the HSPA overlays on the 3G network. Illustrations of the most significant of the updated forecasts in v7.1a of the model are described in more detail below, namely: population low-speed data high-speed data. Population We note that the long-term projected endpoint of 5 million used in the v7.1 model was exceeded in March 2012, according to Statistisk sentralbyrå. 19 The updated model projects that the population will continue to grow, reaching in 2041, rather than stabilising at 5 million as was assumed in the v7.1 model. As stated above, this is consistent with the population forecast used in the fixed LRIC model. 19

31 Population (millions) Low-speed MB (billions) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Figure 3.2: Comparison of population forecasts [Source: NPT LRIC model] Population -Year end v7.1a Population -Year end v7.1 Low-speed data Historical data for demonstrated a sharp increase in low-speed data usage. As a result, we revised upwards our forecasts for low-speed data per subscriber per month; combined with the increase in the forecast subscriber numbers, this leads to significant growth in total lowspeed data megabytes, as shown below Figure 3.3: Comparison of total forecast lowspeed data across Telenor, NetCom and the third operator [Source: NPT LRIC model, 2012] v7.1a v For the demand update, we have assumed that the data labelled Ordinære mobiltelefoniabonnement in NPT s market statistics is low-speed data.

32 High-speed MB (billions) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 29 High-speed data Historical data provided for indicated slower growth in total high-speed data megabytes than anticipated though not as slow as the fall in mobile broadband penetration (yearend) between v7.1 and v7.1a would have led us to expect. As a result, we revised the forecast for another input to high-speed data, mobile broadband HSPA. Here we increased the forecast megabytes per high-speed subscription per month in 2012 from 1000 to The same endpoint is still reached by Coupled with the increased population growth described above, this ultimately leads to the forecast high-speed megabytes in the v7.1a model overtaking those in the v7.1 model in The high-speed data megabytes forecasts from the v7.1 and v7.1a models are compared in Figure 3.4 below Figure 3.4: Comparison of total forecast highspeed data megabytes (excluding ICE) [Source: NPT LRIC model, 2012] v7.1a v The cost of termination under different coverage scenarios The LRIC model calculates the economic costs of Telenor, NetCom and a third operator under certain coverage assumptions for the third operator. Having updated the market data in the LRIC model, we next considered four population coverage scenarios for the third operator: Scenario 1: 71% population coverage, assuming a sub-national network, with close to 100% coverage in the main six Fylker and at least 50% coverage in other Fylker using only 2100MHz frequencies. Scenario 2: 87% population coverage, assuming a sub-national network with close to 100% coverage in the main six Fylker and at least 75% coverage in other Fylker using only 2100MHz frequencies. 21 For the demand update, we have assumed that the data labelled Dedikerte abonnement for mobilt bredbånd in NPT s market statistics is high-speed data.

33 Cost of termination (nominal NOK) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 30 Scenario 3: 95% population coverage, assuming a sub-national network with 3G infrastructure using only 2100MHz frequencies. Scenario 4: 99.99% population coverage, assuming a national network with 3G infrastructure using both 2100MHz and 900MHz frequencies. Scenarios 1 3 assume that an increasing amount of coverage is achieved for 3G, but using 2100MHz frequencies only. These three scenarios are extensions of the sub-national network encoded within the v7.1 LRIC model. In scenario 4, we extend the modelled 3G network to full national coverage in all Fylker (at which national roaming is no longer required). It assumes that 900MHz frequencies are used for coverage to the most rural areas of the country. This assumption includes those six Fylker where the third operator also uses 900MHz for 2G coverage, but 3G 900MHz coverage in these six Fylker is restricted to those areas outside the 2G coverage i.e. there is no overlap. We calculated the unit LRAIC (long run average incremental cost) of termination for each of these scenarios using the v7.1a model. We also calculated the marked-up LRAIC (LRAIC ) for each case. (Although LRAIC is the measure that NPT is using for future regulation of mobile voice termination, we believe that the LRAIC+++ also has particular relevance given that it captures the entire cost base of the network, including common costs.) Figure 3.5 shows the results for Scenario 1, where the third operator has a sub-national network, while Figure 3.6 shows Scenarios 4, where it achieves nationwide coverage. Figure 3.5: Incremental costs of termination for a sub-national third operator network (Scenario 1) [Source: NPT v7.1a LRIC model, 2012] LRAIC LRAIC The three mark-ups specified by the +++ relate to network common costs, location updates and business overheads.

34 Cost of service (Nominal NOK) Cost of termination (nominal NOK) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 31 Figure 3.6: Incremental costs of termination for a national third operator network (Scenario 4) [Source: NPT v7.1a LRIC model, 2012] LRAIC LRAIC+++ It can be seen that Scenario 1 has a lower LRAIC+++ of termination in the long run, but only a slightly lower LRAIC. This is because LRAIC does not include the assets in the coverage network, which is added as one of the mark-ups in the LRAIC+++. In Scenario 4, the coverage network is significantly larger. For each of the four scenarios, we have derived the LRAIC+++ of off-net voice, off-net SMS and data megabytes. As shown in Figure 3.7, the unit costs of all the services increase as the population coverage increases from 87% to 95%, and then further to 100%. Figure 3.7: Output costs of voice, SMS and data in 2012 for the four scenarios [Source: NPT v7.1a LRIC model, 2012] Population coverage Off-net voice Off-net SMS Data We note in particular that the network investment required to achieve higher levels of population coverage increases significantly as coverage reaches 75% up to 100%, but particularly as it rises

35 Capex required, (real 2012 NOK, billions) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 32 above 95%. Figure 3.8 illustrates these investment costs for the third operator. This chart is an estimate of investment derived using the third-operator (national) calculation originally set up in the v7.1 LRIC-model. This calculation assumes that population coverage to all Fylker is deployed relatively evenly, with only a slight preference to roll out coverage to the more urban Fylker (e.g. Oslo). Therefore, an assumed level of 85% population coverage more or less corresponds to 85% population coverage across all Fylker, rather than (say) more than 95% population coverage of the most urban Fylker and lower coverage in the remaining Fylker. A chart where high coverage in the urban coverage is prioritised would have a lower gradient between 75% and 85% population coverage. Examples of such deployments are considered in Section 3.3 below. Figure 3.8: Increase in investment costs with population coverage for the third operator [Source: NPT v7.1a LRIC model, 2012] Population coverage 3.3 Total mobile economic cost Depending on the assumed level of coverage of the third operator, some of its retail market share of traffic will be offloaded onto the Telenor and NetCom networks as national roaming traffic. If this traffic cannot all be carried by just using existing capacity in the other two networks, then additional network costs would be induced in the other two networks. There is therefore a potential trade-off in the mobile market between (a) a situation in which the third operator has a smaller network footprint (thus reducing its own network costs but potentially increasing the costs of the other two networks), and (b) a situation in which the third operator rolls out a larger network (reducing costs for the other two networks but increasing its own). We have investigated this trade-off by considering different coverage profiles for the third operator. Each profile assumes that a range of particular values of long-term coverage (up to 100%) are achieved using UMTS operating in both 900MHz and 2100MHz frequencies. The GSM deployment assumptions are left unchanged. For each particular value of long-term third-operator coverage, we have calculated the total economic costs across all three modelled operators. This total is referred to as total mobile economic cost or TMEC.

36 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 33 In the following, we illustrate the TMEC summed across the three market operators. This is calculated as the economic costs for (i.e. from the modelling perspective, in perpetuity), discounted to 2012 using the 10.7% real discount rate assumed in the LRIC model. This discount rate is a consistent measure of network cost of capital to the mobile industry as a whole. We have also calculated the number of NodeBs the third operator deploys to achieve each level of coverage in the profile, since these assets are a significant driver of total network cost. This allows a curve to be drawn of TMEC against third-operator population coverage. A minimum on this curve corresponds to a level of population coverage by the third operator at which all traffic in the market is conveyed by the three networks most efficiently. Technically, such a point is referred to as static productive efficiency. As part of this analysis, we have classified the Fylker in Norway into three categories: Major Fylker, which contain six of the largest cities in Norway (Akershus, Hordaland, Oslo, Rogaland, Sor Trøndelag and Vestfold), which were identified in the work on the LRIC model in Northern Fylker (Nord-Trøndelag, Nordland, Troms, Finnmark and Svalbard) Other Fylker, which are the remainder (Aust-Agder, Buskerud, Hedmark, More Og Romsdal, Oppland, Ostfold, Sogn Og Fjordane, Telemark and Vest-Agder). We have considered three coverage profiles for increasing population coverage by the third operator (note that these coverage profiles A, B and C are not related to scenarios 1-4 considered above). These coverage profiles include variations on both the 2100MHz-only 3G coverage, and the total 3G coverage (2100MHz+900MHz). These 2100MHz 3G coverage and total 3G coverage assumptions are used in rows 868 and 923 of the A4_NtwDesBase worksheet in the v7.1a model respectively. Coverage profile A, where 2100MHz 3G coverage is assumed to be static in Major Fylker at 98% of population, and static in Other/Northern Fylker once it has reached 40% of population. Total 3G coverage first increases to 98% of population in all Fylker, and then grows to 100% of population in all Fylker. These increases are achieved using 900MHz frequencies. Coverage profile B, where 2100MHz 3G coverage is assumed to be static at 98% in Major Fylker and zero in the Other/Northern Fylker. Total 3G coverage increases across all Fylker, which is achieved using 900MHz frequencies. Coverage profile C, where 2100MHz 3G coverage behaves as in Coverage profile A, whilst total 3G coverage increases first in Major Fylker to 100% and only then increases in Other/Northern Fylker. These increases are achieved using 900MHz frequencies responses_v7%201_public_ pdf, Section 4.5.

37 Total Major Fylke coverage Total Major Fylke coverage Total Major Fylke coverage Total Other / Northern Fylke coverage Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 34 We describe these coverage profiles in more detail below and illustrate the inputs used for the third operator. For each of these coverage profiles, we have examined three different assumptions for the treatment of national roaming traffic: Assumption 1, 0% on Telenor : Telenor carries 0% of this traffic (i.e. NetCom carries 100%) Assumption 2, 50% on Telenor : Telenor and NetCom each carry 50% of this traffic Assumption 3, 100% on Telenor : Telenor carries 100% of this traffic. Assumption 2 is what the v7.1 LRIC model implicitly assumed, on the basis that Telenor and NetCom have national roaming agreements with Network Norway and Tele2 respectively and therefore should theoretically each have complementary shares of the national roaming traffic. In this case, traffic roams onto the networks of both Telenor and NetCom. This additional loading could potentially be accommodated by the deployed capacity of these two operators. Assumptions 1 and 3 test more extreme cases, where all of the national roaming traffic is applied to one operator (either NetCom or Telenor), increasing the likelihood that additional capacity deployments are required on the host operator s network Coverage profile A Figure 3.9 below illustrates the coverage assumptions used for the Major Fylke, in terms of 2100MHz coverage and incremental 900MHz coverage. Figure 3.10 below illustrates the corresponding assumptions for Other Fylker and Northern Fylker. Figure 3.9: Assumptions for 3G population coverage in Major Fylker in Coverage profile A [Source: Analysys Mason, 2012] 100% 100% 90% 90% Figure 3.10: Assumptions for 3G population coverage in Other/Northern Fylker in Coverage profile A [Source: Analysys Mason, 2012] 100% 100% 90% 90% 80% 80% 70% 70% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% 2100MHz coverage Incremental 900MHz coverage 2100MHz coverage Incremental 900MHz coverage So, this coverage profile assumes some 2100MHz coverage in all Fylker, as well as some 900MHz coverage in all Fylker. The curves of the TMEC using these coverage assumptions are illustrated in Figure 3.11 below. For each assumption, the dotted line indicates the level of population coverage by the third operator at which the TMEC is minimised. We observe that the TMEC decreases as the population coverage of the third operator increases between 50% coverage and at

38 Total mobile economic cost (billions) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 35 least 75% coverage. This is a result of better utilisation of the fixed costs of the third operator, as well as better utilisation of its spectrum allocation, while simultaneously not causing additional costs from national roaming traffic in the other operators networks Figure 3.11: TMEC curve for coverage profiles within Coverage profile A under different national roaming assumptions [Source: NPT v7.1a LRIC model, 2012] Total 3G population coverage 100% on Telenor 50% on Telenor 0% on Telenor In Figure 3.12 below we illustrate the number of NodeBs deployed by the third operator in all the cases shown above. For a given level of third-operator coverage, the number of NodeBs is the same regardless of whether 100%, 50% or 0% of national roaming traffic is carried on Telenor: therefore the one curve in Figure 3.12 underlies each of the three curves in Figure 3.11 above. The curve below correlates closely to the three TMEC curves above, especially when population coverage is over 85%. We note that this analysis indicates that approximately 2300 NodeBs are required for 75% overall coverage (modelled as 98% coverage just in the main six Fylker).. However, this is due in the model to the Oslo Fylke being assumed to be 100% covered in area terms, and 20% of this area coverage is achieved by using in-fill NodeBs with a smaller coverage area. The model assumes the Oslo coverage network is effectively being densified by these extra NodeBs.

39 Third operator Node Bs Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Figure 3.12: NodeBs deployed for 3G coverage profiles within Coverage profile A [Source: NPT v7.1a LRIC model, 2012] Total 3G population coverage As can be seen from Figure 3.11 above, the TMEC is minimised between 85% and 90% of population coverage for the third operator. It is also notable that in all three cases the TMEC curves tend to plateau at around the minimum value of the TMEC, meaning that there is little change in TMEC as third-operator coverage varies. This is particularly true when at least 50% of traffic is being carried on Telenor s network. TMEC increases significantly when the third operator exceeds 95% coverage, which suggests that high levels of coverage (nearly nationwide) do not lead to static productive efficiency Coverage profile B Figure 3.13 below illustrates the coverage assumptions used for the Major Fylke, in terms of 2100MHz coverage and incremental 900MHz coverage. Figure 3.14 below illustrates the corresponding assumptions for Other Fylker and Northern Fylker.

40 Total Major Fylke coverage Total Major Fylke coverage Total mobile economic cost (billions) Total Major Fylke coverage Total Other / Northern Fylke coverage Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 37 Figure 3.13: Assumptions for 3G population coverage in Major Fylker in Coverage profile B [Source: Analysys Mason, 2012] 100% 100% 90% 90% 80% 80% Figure 3.14: Assumptions for 3G population coverage in Other / Northern Fylker in Coverage profile B [Source: Analysys Mason, 2012] 100% 100% 90% 90% 80% 80% 70% 70% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% 2100MHz coverage Incremental 900MHz coverage 2100MHz coverage Incremental 900MHz coverage The curves of the TMEC using these coverage assumptions and including all Fylker are illustrated in Figure 3.15 below. As before, the dotted lines indicate the coverage level at which the TMEC is minimised. In Figure 3.16 we illustrate the third-operator NodeBs required under these assumptions (note that the same number is required regardless of whether 100%, 50% or 0% of national roaming traffic is carried on Telenor s network) Figure 3.15: TMEC curve for coverage profiles within Coverage profile B under different national roaming assumptions [Source: NPT v7.1a LRIC model, 2012] Total 3G population coverage 100% on Telenor 50% on Telenor 0% on Telenor

41 Total Major Fylke coverage Total Major Fylke coverage Third operator Node Bs Total Major Fylke coverage Total Other / Northern Fylke coverage Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Figure 3.16: NodeBs deployed for 3G coverage profiles within Coverage profile B [Source: NPT v7.1a LRIC model, 2012] Total 3G population coverage The minimum TMEC occurs within almost the same range here as in Coverage profile A: there is no significant difference in the TMEC curves since the only major difference between Coverage profiles A and B is that Coverage profile B covers the first 40% of Other/Northern Fylker with 900MHz, rather than 2100MHz Coverage profile C Figure 3.17 below illustrates the coverage assumptions used for the Major Fylke, in terms of 2100MHz coverage and incremental 900MHz coverage. Figure 3.18 below illustrates the corresponding assumptions for Other Fylker and Northern Fylker. These coverage assumptions (Figure 3.17) are similar to Coverage profile A, except that the increase to 100% coverage in the Major Fylker is prioritised earlier. Figure 3.17: Assumptions for 3G population coverage in Major Fylker in Coverage profile C [Source: Analysys Mason, 2012] 100% 100% 90% 90% Figure 3.18: Assumptions for 3G population coverage in Other / Northern Fylker in Coverage profile C [Source: Analysys Mason, 2012] 100% 100% 90% 90% 80% 80% 70% 70% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% 2100MHz coverage Incremental 900MHz coverage 2100MHz coverage Incremental 900MHz coverage

42 Total mobile economic cost (billions) Third operator Node Bs Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 39 The TMEC results under these assumptions can be seen in Figure 3.19, and the number of NodeBs required is shown in Figure Again, the number of NodeBs required for a given level of 3G coverage is the same regardless of whether 100%, 50% or 0% of national roaming traffic is carried on Telenor s network. At approximately 70% population coverage, the last 2% of population coverage in the six Major Fylker are being covered, before deploying coverage to the Other Fylker. This requires covering the least densely populated areas of the Major Fylker. A large number of sites must be deployed in order to cover these areas, meaning a steep increase in the number of NodeBs for a small percentage of total population coverage, and a corresponding steep abrupt increase in the TMEC Figure 3.19: TMEC curve for coverage profiles within Coverage profile C under different national roaming assumptions [Source: NPT v7.1a LRIC model, 2012] Total 3G population coverage 100% on Telenor 50% on Telenor 0% on Telenor Figure 3.20: NodeBs deployed for 3G coverage profiles within Coverage profile C [Source: NPT v7.1a LRIC model, 2012] Total 3G population coverage

43 Third operator Node Bs Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Conclusions Our analysis shows that there does appear to be a minimum level of TMEC across the range of third-operator population coverage, although the curves are fairly shallow (particularly when at least 50% of the offloaded traffic is carried on Telenor s network). This indicates that there are a range of population coverage levels that result in similar economic efficiency. For all coverage profiles, the TMEC increases significantly for coverage of the last 5% of population. The most important point illustrated by Coverage profile C is the steep increase in the TMEC when covering the last 2% of the population in the Major Fylker, coupled with a slow decrease in the TMEC as the most urban parts of the Other/Northern Fylker are covered. This indicates that it is more efficient to cover the most urban parts of Other/Northern Fylker first rather than cover all population in the Major Fylker. This means that prioritising perfect coverage over widespread coverage as applied in Coverage profile C increases overall costs without significantly increasing the population able to benefit from the roll-out of the third operator s network. In parallel to this analysis, we would also note that there are benefits to deploying coverage to the Northern Fylker, as the most urban parts of these Fylker can be covered with a small number of base stations, particularly with 900MHz frequencies, as illustrated in Figure 3.21 below. 12% of Norway s population lie within these Fylker, and almost 11% out of this 12% can be covered with approximately 50 NodeBs using 900MHz frequencies Figure 3.21: NodeBs required to cover Northern Fylker (coverage specified is proportion of total Norway population) [Source: NPT v7.1a LRIC model, 2012] MHz 900MHz

44 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 41 4 Network coverage This chapter describes our main findings from the calculations that we have undertaken of the area and population coverage of the mobile network technologies operated in Norway. This material is covered in more detail in Annex A. Section 4.1 describes the data and assumptions used in the calculation, detailing their sources and any manipulation of the raw data Section 4.2 outlines the main results from our coverage calculations Section 4.3 presents our conclusions. 4.1 Inputs The main inputs used in the coverage calculation are: The location of base stations in Norway, using operator data for year-end 2011 Assumed coverage radii for base stations by technology, which we have calibrated using the actual coverage of the operators, as described in more detail in Annex A. A geographic dataset of Norway split by grunnkrets, the areas used by the Statistisk sentralbyrå (SSB), with population and area for each area. Figure 4.1 below shows the totality of sites used by the four mobile network operators (Telenor, NetCom, Mobile Norway and ICE). Some of these sites contain installations for only one technology for one operator, some for multiple technologies for one operator, and some are co-used by multiple operators. Figure 4.1: Base station sites in Norway [Source:, Statistisk sentralbyrå, Analysys Mason, 2012] We recognise that definitions of land area coverage in Norway can also include areas of sea between the islands and around the coast. This additional coverage should stop at the water ground line (also known as the base line ). We have estimated this additional area, as shown in Figure 4.1 above. These additional regions have been illustrated in blue above. This allows us to calculate coverage of any given network technology, split by physical land coverage and additional coverage to the base line.

45 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Coverage calculations for the operators We have calculated network coverage using two methods: Method A calculates coverage by Fylke, with all coverage by base stations captured. Method B also calculates coverage by Fylke, but only includes coverage in a Fylke from base stations that lie within that Fylke. These methods, using the datasets described in Annex A, identify the population and area that lie within the coverage shapes of the operators networks. Method B will give lower values of coverage by definition, and was used in the coverage calculations undertaken for the LRIC model in previous projects. In the figures below, base line area coverage includes the sea areas illustrated in Figure 4.1. The most important results are in Figure 4.2 below, which illustrates the coverage for Mobile Norway networks using these methods. Our results are presented in more detail in Annex A. Figure 4.2: Coverage of the Mobile Norway networks [Source: Analysys Mason, 2012] 2G network 3G network 2G+3G networks Area (Method A) Area (Method B) Base line (Method A) Base line (Method B) Population (Method A) Population (Method B) We have also calculated the level of overlap between the network coverage of the operators. Figure 4.3 illustrates the coverage for the network using Method A, for Telenor, NetCom and Mobile Norway (ICE is excluded). Figure 4.3: Land area and population of Norway covered by number of operators (excluding ICE) [Source: Analysys Mason, 2012] Number of operators Coverage Zero One Two Three 2G-only area 3G-only area 2G-only population 3G-only population

46 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Conclusions In addition, as can be seen using Figure 4.2 and Figure 4.3 above, Mobile Norway s 3G population coverage is estimated to be %. However, % of Norway s population is covered by all three of Telenor, NetCom and Mobile Norway s networks, meaning that currently Mobile Norway s own coverage almost fully duplicates the coverage of both Telenor and NetCom. Mobile Norway so far does not appear to be seeking to cover Norwegian population that is not already covered by both the other operators.

47 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 44 5 Network capacity This chapter describes our investigations into the existing capacity within the mobile networks of Telenor and NetCom, from the perspective of both voice and data. Section 5.1 outlines the inputs provided by the operators Section 5.2 describes our capacity calculations for 3G networks Section 5.3 describes our capacity calculations for 2G networks Section 5.4 provides our conclusions. 5.1 Inputs In order to understand the capacity of the mobile networks deployed in Norway, NPT requested that Telenor, NetCom and Mobile Norway provide information regarding their 2G base stations and 3G base stations. The formats of these data requests are shown below. TRXs Busy-hour (BH) voice Erlangs BHMB Figure 5.1: Inputs requested for 2G base stations [Source: Analysys Mason, 2012] BH voice Erlangs BHMB Activated channel elements Installed HSDPA speed (Mbit/s) Figure 5.2: Inputs requested for 3G base stations [Source: Analysys Mason, 2012] Data was received from all three operators;. On at least one occasion, operators provided daily traffic rather than busy-hour megabytes. The figures were converted into busy-hour equivalents using the input data from the appropriate operator cost calculation in NPT s LRIC model, for the proportion of daily traffic that is in the busy-hour (on a site-by-site level). We first of all note that we considered using the Erlang table from the LRIC model to directly derive the number of TRXs and channel elements (CEs) required for the provided values of Erlang loading. However, the Erlang table in the model is designed to give an average number of TRX/CE per base station for an average number of Erlangs per base station, whereas this operator data is provided on a sector-by-sector basis. Therefore, we have investigated other approaches, as discussed below.

48 BHE per sector Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 45 In the following two sections, we describe our capacity calculations for 3G and 2G networks in turn. 5.2 Capacity calculations for the 3G networks We note that, in Norway, it often appears standard practice to deploy omni-sectored base stations primarily for coverage, and then to sectorise these base stations as they require more capacity. Therefore, the number of sectors deployed on a base station is often an indicator of the capacity required. Figure 5.3 below illustrates the sectorisation of 3G base stations for Telenor and NetCom (single-sectored NodeBs are shown as smaller points to better illustrate their location in relation to the other NodeBs deployed). Figure 5.3: Base stations by number of sectors in 3G networks for Telenor (left) and NetCom (right) [Source: Analysys Mason, 2012]. Figure 5.4 below illustrates the average voice busy-hour Erlangs (BHE) per sector for each of these categories of NodeB in s network. As can be seen, for all categories the average BHE per sector is Erlangs. Figure 5.4: BHE per sector and average sectors per base station, for the three main categories of CE deployment in s 3G network [Source: Analysys Mason, 2012] Sectors per BTS Figure 5.5 below illustrates a similar calculation of average busy-hour megabytes (BHMB) per sector for each of these categories of NodeB in s network. As can be seen, the average BHMB per sector is megabytes for all categories of NodeBs.

49 BH MB per sector Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 46 Figure 5.5: BHMB per sector and average sectors per base station, for the three main categories of CE deployment in s 3G network [Source: Analysys Mason, 2012] Sectors per BTS As shown in Figure 5.6 below, for both Telenor s and NetCom s 3G networks, plotting the data loading (BHMB per sector) against the voice loading (BHE per sector) for each 3G base station results in a fan-like distribution. Figure 5.6: 3G network loading by base station for Telenor (top) and NetCom (bottom); same scales used for the corresponding axes in both charts [Source: Analysys Mason, 2012] We have further categorised all NodeBs based on these two loading dimensions, in increments of 1 BHE and 100 BHMB. Since, as already determined, the number of sectors is also a driver of 3G capacity (), we have captured this as a third dimension, by classifying 1-sectored, 2-sectored and 3-sectored NodeBs separately. In each case, we classify NodeBs according to intervals of 1 BHE and 100 BHMB. We have then estimated (a) which classes are likely to have free capacity, and (b) which classes are unlikely to have free capacity. These estimations are based on maximum BHE per sector and BHMB per sector using s data above. We have developed three scenarios for this free capacity. In Scenario 2, we make the following assumptions: A NodeB is estimated as likely to have free capacity if the average BHE per sector is less than 4 BHE and if the average BHMB per sector is less than 800 megabytes (4 BHE and 800 megabytes are lower bounds for the average values illustrated in Figure 5.4 and Figure 5.5 respectively). A NodeB is estimated as unlikely to have free capacity if the average BHE per sector is more than 8 BHE and if the average BHMB per sector is more than 1600 megabytes. Scenario 1 is more conservative, with both of these assumptions reduced by 25%. We consider these two scenarios below, in Section and We note that both are conservative in terms of the potential capacity of the NodeBs deployed, given that the distributions shown in Figure 5.6

50 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 47 above indicate that technically identical sectors can support significantly higher traffic volumes than our assumed thresholds. This means that our calculation has a degree of future-proofing, being relatively unaffected by the fact that traffic within Norway will grow further between 2011 and Furthermore, the loading data provided by Telenor and NetCom also includes the national roaming traffic already present on their respective networks at the end of Scenario 3 We have also modelled a third scenario (see Section 5.2.3) to consider seasonality effects in network traffic, particularly in popular holiday areas. For this scenario we have multiplied all of the Telenor and NetCom sector-by-sector voice and data traffic by a factor of and then re-run the Scenario 1 calculations Scenario 1: NodeBs with less than 3 BHE and 600 BHMB per sector have free capacity An example of the classification for one operator s 1-sectored NodeBs is shown in Figure 5.7. All the NodeBs inside the green box in the diagram are likely to have free capacity (as they have a loading of less than 3 BHE and less than 600 BHMB), whilst all NodeBs outside the red box are unlikely to have free capacity (as they have a loading of more than 6 BHE and more than 1200 BHMB). The remaining NodeBs do not definitely fall into either category, although there may be the possibility of free capacity at these installations. Since. We have then identified both the Telenor and NetCom NodeBs that we believe could have free capacity in the busy-hour and those that we believe do not have free capacity. We have done this for two cases: (a) for the entire Telenor/NetCom network and (b) for the Telenor/NetCom network outside of our estimate of the Mobile Norway s coverage network. Erlangs Megabytes Figure 5.7: Categorisation* of 1-sectored NodeBs by voice BHE loading and busy-hour megabyte loading for one operator [Source: Analysys Mason, 2012] * Each blue cell represents a count of NodeBs (the darker the colour, the higher the count of NodeBs in that category of traffic loading. This count is therefore a grouping of the detailed fan-shaped distribution of site by traffic 24 This factor has been derived from.

51 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 48 Analysis of Telenor/NetCom NodeBs across the whole country We have identified the Telenor and NetCom NodeBs that are likely to have free capacity in the busy-hour, and those that are unlikely to have free capacity in the busy-hour. Figure 5.8 below shows the proportion of the two operators installations (in terms of their NodeBs and sectors) estimated to have (or not have) free capacity in the busy-hour. Figures are shown for each region and for Norway as a whole. Figure 5.8: Proportion of Telenor/NetCom installations estimated as having free capacity or not (in terms of their NodeBs and the sectors on those NodeBs) [Source: Analysys Mason, 2012] Region Proportion with free capacity Proportion with no free capacity NodeBs Sectors NodeBs Sectors East (Hedmark, Oppland) 42% 51% 31% 25% South-East (Akershus, Oslo, Ostfold and Vestfold) 38% 41% 29% 28% South (Aust-Agder, Buskerud, Telemark, Vest-Agder) 38% 40% 23% 22% West (Hordaland, Rogaland, Sogn Og Fjordane) 54% 57% 21% 20% North-West (More Og Romsdal, Sor-Trøndelag) 37% 40% 32% 29% North (Finnmark, Nord-Trøndelag, Nordland, Troms) 39% 42% 27% 24% Norway (absolute numbers in brackets) 41%() 44%() 28%() 25%() As described above, this analysis is made on a node-by-node basis. As can be seen in Figure 5.6 above,. this situation means that the proportion of sectors likely to have free capacity is usually higher than the proportion of NodeBs that are likely to have free capacity. Figure 5.9 below illustrates that the NodeBs likely to have free capacity are distributed across the whole country. Figure 5.9: Telenor and NetCom NodeBs by estimated capacity status, shown alongside Mobile Norway s NodeBs [Source: Analysys Mason, 2012] KEY No free capacity Unknown capacity status Free capacity Mobile Norway NodeBs Analysis of Telenor/NetCom NodeBs outside Mobile Norway s coverage area We have carried out the same analysis, but examining only those Telenor and NetCom NodeBs that lie outside an estimation of Mobile Norway s coverage area. Our estimation assumes the Mobile Norway 3G coverage network used to derive the values in Section 4, but with the cell radii reduced by 50%. This value may appear somewhat aggressive, but is intended to have two purposes: (a) to approximate the effects of cell breathing due to the peak network traffic loading during the network busy hour; and, more importantly (b) to ensure that Telenor and NetCom NodeBs at the fringes of Mobile Norway s coverage are still taken into account.

52 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 49 Figure 5.10 shows the proportion of Telenor and NetCom installations (in terms of their NodeBs and sectors) likely to have (or not have) free capacity in the busy-hour. The figures are broken down by region. Figure 5.10: Proportion of Telenor/NetCom installations estimated to have free capacity (or not) in terms of total numbers of NodeBs and sectors, and located outside Mobile Norway s 3G coverage area [Source: Analysys Mason, 2012] Region Proportion with free capacity Proportion with no free capacity NodeBs Sectors NodeBs Sectors East (Hedmark, Oppland) 50% 61% 24% 18% South-East (Akershus, Oslo, Ostfold and Vestfold) 37% 40% 30% 29% South (Aust-Agder, Buskerud, Telemark, Vest-Agder) 38% 41% 27% 25% West (Hordaland, Rogaland, Sogn Og Fjordane) 69% 75% 9% 7% North-West (More Og Romsdal, Sor-Trøndelag) 51% 56% 25% 22% North (Finnmark, Nord-Trøndelag, Nordland, Troms) 43% 46% 26% 24% Norway (absolute numbers in brackets) 46% () 52% () 24% () 22% () Once more, the analysis shows that the Telenor and NetCom NodeBs with free capacity that are outside of Mobile Norway s coverage area are distributed across the whole country (see the map in Figure 5.11 below). All regions contain a mix of sites that are likely have free capacity, sites likely to have no free capacity and sites that do not definitely fall into either category. However, closer examination of the data in Figure 5.8 and Figure 5.10 indicates that a greater proportion of installations with free capacity fall in the areas outside Mobile Norway s coverage. This can be seen in the West region, for example. Here 54% of sites are estimated to have free capacity, but when only the installations outside Mobile Norway s coverage area are considered, this proportion increases to 69% of NodeBs and 75% of sectors. This effect appears to occur in all regions apart from South-East Norway. Figure 5.11: Telenor and NetCom NodeBs outside Mobile Norway s coverage area, broken down by estimated capacity status (coloured dots), shown against Mobile Norway s NodeBs (black dots) [Source: Analysys Mason, 2012] KEY No free capacity Unknown capacity status Free capacity Mobile Norway NodeBs Scenario 2: NodeBs with less than 4 BHE and 800 BHMB per sector have free capacity An example of the classification for one operator s 1-sectored NodeBs is shown in Figure The NodeBs within the green box are likely to have free capacity (as they have less than 4 BHE and less than 800 BHMB), whilst NodeBs outside the red box are likely to not have free capacity

53 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 50 (as they have more than 8 BHE and more than 1600 BHMB). The remaining NodeBs do not definitely fall into either category. Since. Figure 5.12: Categorisation of 1- sectored NodeBs by voice BHE loading and busy-hour megabyte loading for one operator [Source: Analysys Mason, 2012] Analysis of Telenor/NetCom NodeBs across the whole country Figure 5.13 below shows the proportion of Telenor and NetCom installations that are likely to have (or not have) free capacity in the busy-hour (in terms of their NodeBs and sectors). Figures are broken down by region. Figure 5.13: Proportion of Telenor/NetCom installations having free capacity or not (in terms of their NodeBs and the sectors on those NodeBs) [Source: Analysys Mason, 2012] Region Proportion with free capacity Proportion with no free capacity NodeBs Sectors NodeBs Sectors East (Hedmark, Oppland) 54% 62% 23% 19% South-East (Akershus, Oslo, Ostfold and Vestfold) 53% 55% 20% 20% South (Aust-Agder, Buskerud, Telemark, Vest-Agder) 56% 59% 15% 15% West (Hordaland, Rogaland, Sogn Og Fjordane) 65% 67% 15% 15% North-West (More Og Romsdal, Sor-Trøndelag) 49% 53% 23% 21% North (Finnmark, Nord-Trøndelag, Nordland, Troms) 55% 60% 17% 16% Norway (absolute numbers in brackets) 55% () 58% () 19% () 18% () Figure 5.14 below illustrates that the NodeBs in Telenor s and NetCom s network that we estimate as having free capacity are present across the whole country.

54 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 51 Figure 5.14: Telenor and NetCom NodeBs broken down by estimated capacity status (coloured dots), shown against Mobile Norway NodeBs (black dots) [Source: Analysys Mason, 2012] KEY No free capacity Unknown capacity status Free capacity Mobile Norway NodeBs Analysis of Telenor/NetCom NodeBs outside Mobile Norway s coverage area Figure 5.15 below shows the proportion of Telenor and NetCom installations that are likely to have (or not have) free capacity in the busy-hour, and that are located outside of Mobile Norway s coverage area. As for Scenario 1, this analysis uses the 3G coverage network used to derive the values in Section 4, but with the cell radii reduced by 50%. Figure 5.15: Proportion of Telenor/NetCom installations estimated as having free capacity (or not) in terms of their NodeBs and the sectors on those NodeBs, and located outside Mobile Norway s 3G coverage area [Source: Analysys Mason, 2012] Region Proportion with free capacity Proportion with no free capacity NodeBs Sectors NodeBs Sectors East (Hedmark, Oppland) 62% 72% 18% 15% South-East (Akershus, Oslo, Ostfold and Vestfold) 52% 54% 20% 20% South (Aust-Agder, Buskerud, Telemark, Vest-Agder) 56% 59% 18% 18% West (Hordaland, Rogaland, Sogn Og Fjordane) 81% 85% 6% 5% North-West (More Og Romsdal, Sor-Trøndelag) 62% 68% 20% 18% North (Finnmark, Nord-Trøndelag, Nordland, Troms) 58% 62% 17% 17% Norway (absolute numbers in brackets) 60% () 65% () 17% () 16% () Figure 5.16 below shows that the NodeBs in Telenor s and NetCom s network that are estimated as having free capacity are present across the whole country. Figure 5.16: Telenor and NetCom NodeBs outside the Mobile Norway coverage area, broken down by estimated capacity status (coloured dots), and shown against Mobile Norway NodeBs (black dots) [Source: Analysys Mason, 2012] KEY No free capacity Unknown capacity status Free capacity Mobile Norway NodeBs As for Scenario 1, of sites that are likely have free capacity, sites likely to have no free capacity and sites that do not definitely fall into either category. Once more, however, a greater proportion of installations having free capacity are located outside Mobile Norway s coverage area. To take the West region again as an example, 65% of all NodeBs and 67% of all sectors have free capacity, but this proportion increases to 81% and 85% respectively if we only consider installations outside Mobile Norway s coverage area. This effect appears to occur in all regions

55 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 52 apart from South-East Norway, where there is a 1% decrease in the proportion of installations estimated as having free capacity Scenario 3: As for Scenario 1, but with all traffic increased by a factor of 2.5 We have also analysed a third scenario, designed to take into consideration certain seasonality effects in network traffic. Some areas of Norway for example the coast of Telemark have a significant number of holiday homes. In these areas, networks will be more heavily loaded during the summer holiday season than at other times. However, the data used in our calculations for Scenarios 1 and 2 comes from the first quarter of 2012 months when there was relatively little network traffic in these holiday areas. Although the maps in Sections and showed significant numbers of sites with free capacity in these areas, there remains the question of how the picture changes during the holiday season. We have attempted to investigate this by repeating the Scenario 1 capacity calculation with both voice and data traffic on the Telenor and NetCom sectors increased by a factor of 2.5. This mark-up is intended to reflect the higher loading in holiday areas during the summer months. Figure 5.17 shows the classification of one operator s 1-sectored NodeBs under the revised traffic assumptions. The parameters for whether or not a sector has free capacity are the same as in Scenario Erlangs Megabytes Figure 5.17: Categorisation of 1-sectored NodeBs by voice BHE loading and busy-hour megabyte loading for one operator [Source: Analysys Mason, 2012] 25 A NodeB is considered likely to have free capacity if it has less than 3 BHE and less than 600 BHMB. It is unlikely to have free capacity if it has more than 6 BHE and more than 1200 BHMB.

56 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 53 We have then identified the Telenor and NetCom NodeBs that are likely to have (or not have) free capacity in the busy-hour, and that are located outside of Mobile Norway s coverage network. 26 Figure 5.18 below shows the results. Figure 5.18: Telenor and NetCom NodeBs outside Mobile Norway s coverage area broken down by estimated capacity status (coloured dots), and shown against Mobile Norway NodeBs (black dots) [Source: Analysys Mason, 2012] KEY No free capacity Unknown capacity status Free capacity Mobile Norway NodeBs As can be seen from the magnified inset showing the coast of Telemark, as an example, there are still a number of green dots in this area (which were also present in Scenarios 1 and 2) and relatively few red dots. This implies that even with a significant increase in traffic in holiday areas, as seen during the summer months, there is still a high level of free capacity in these areas. 5.3 Capacity calculations for the 2G networks We observe that the request to NPT for additional subsidy of the Mobile Norway deployment via continued MTR asymmetry primarily relates to 3G capacity (i.e. additional NodeBs). We do not believe that Mobile Norway has plans for national 2G coverage in the short term. Also,. Therefore we take the view that the free capacity of the 2G networks in Norway is of limited interest in this investigation, since the larger capacity of the 3G networks is most relevant to the question of (spare) capacity. Nonetheless, we have undertaken some analysis of Telenor and NetCom s 2G networks, following a similar approach to that described for their 3G networks in the previous section. Figure 5.19 below illustrates the sectorisation of 2G base stations for Telenor and NetCom. Singlesectored NodeBs are shown as smaller points to better illustrate their location in relation to the other NodeBs deployed. The use of 2-sectored base stations along the highways is apparent, and there is also extensive use of 1-sectored base stations to provide coverage outside of urban areas. (The use of 3-sectored base stations in urban areas is partially obscured, particularly in the Oslo area, due to the scale used.) Figure 5.19: Base stations by number of sectors in 2G networks for Telenor (left) and NetCom (right) [Source: Analysys Mason, 2012] Another important aspect is the balance of voice and data traffic carried by the site, since both will contribute to the total loading of the base station. We have plotted the voice BHE against the data 26 For details of modelling assumptions, please refer to Section

57 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 54 BHMB for each of the base stations in the two 2G networks, and found that they both form fanshaped distributions, as shown below in Figure Figure 5.20: 2G network loading by base station for Telenor (top) and NetCom (bottom); different scales used for the vertical axes [Source: Analysys Mason, 2012] 5.4 Conclusions Our capacity calculations for the 3G networks (Section 5.2) indicate that, even under the conservative capacity assumption of 3 4 BHE of voice and BHMB of data, there is still a significant number of base stations in the Telenor and NetCom networks that we estimate as likely to have free capacity. In particular, in almost all regions of Norway a greater proportion of installations having free capacity are located outside Mobile Norway s coverage area. Our estimations of free capacity are conservative given that they are derived, on average, across all of the NodeBs in s network. The data submitted by indicates that there are NodeBs in its network that can support significantly higher traffic volumes than assumed in our modelling. In addition, the traffic loading which we have been provided with by Telenor and NetCom already includes some national roaming traffic from the subscriber bases of Tele2 and Network Norway (i.e. both sets of subscribers are already occupying capacity on both networks). Figure 5.8 and Figure 5.10 in particular indicate that there is likely to be free capacity in all regions of Norway. We considered a scenario with a traffic uplift of 2.5 in order to examine capacity in areas subject to seasonal effects such as, for example, the Telemark coast. Holiday areas were still estimated as having free capacity. We suggest that, in any case, it is not necessarily clear that infrastructure duplication by the third operator in these areas would be efficient. Figure 5.21 below illustrates the estimated loading on a base station in Oslo versus a base station in a holiday home area. The two will experience opposite seasonal effects, with a slight decline in traffic in Oslo during the holiday period, and an increase in traffic in the holiday area. As a result, the Oslo base station has some additional unused capacity in the summer months, as it must accommodate the higher levels of traffic in the non-summer months. However, if the base station in the holiday area is to be able to carry the peak traffic in the summer months, then this results in significant unused capacity in the non-summer months. Hence, network deployments in such holiday areas will be poorly utilised for many months of the year (to a far greater extent than in Oslo or other urban areas).

58 loading loading Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 55 Figure 5.21: Illustration of difference in used and unused capacity due to seasonal effects in base stations in Oslo versus a holiday home area [Source: Analysys Mason, 2012] Loading over the year on a base station in Oslo Loading over the year on a base station near a holiday home area months months KEY Used capacity Unused capacity We presume that this is already the situation experienced by Telenor and NetCom, so we consider it debateable as to whether it is efficient to have potentially large seasonal infrastructure utilisation effects duplicated on three networks rather than two (or indeed four networks rather than three, if one also takes into account the widespread coverage of ICE in the rural areas of Norway). We also observe that the three networks in Norway are entirely standalone, with no active network-sharing agreements currently in existence. In a number of European countries (such as Sweden, Denmark and the UK), domestic mobile operators have increasingly been entering into (or, in the case of Sweden, have started with) active network-sharing agreements. We believe the benefits of this are more strongly felt in rural areas, where networks will be less capacityconstrained than in urban areas.

59 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 56 6 Margin analysis This chapter describes the analyses that we have undertaken on the potential margins for the third operator, using the margin squeeze model recently developed by Analysys Mason for NPT. We have made a number of refinements to the margin model, for example to allow it to calculate in years other than The margin model uses various outputs from the LRIC model, which we have derived using the v7.1a version of the model. This chapter is structured as follows: Section 6.1 describes the initial refinements that we have made to the margin model Section 6.2 summarises our initial calculations of average margins Section 6.3 presents our conclusions. 6.1 Refinements to the margin model Two new capabilities have been added to the margin model for the purposes of this study. These are the ability to calculate margins for a future year in the model, and the ability to consider the margins for a mobile-broadband-only product. These are described below Margin calculations for a future year (2020) A series of new inputs were added to the Control worksheet of the model in order to calculate the margins of an operator in a chosen future year (rather than just the current year). These additional inputs are as follows: Modelled year: determines the future year for which the model outputs are being calculated. Network coverage as a proportion of traffic: directly changes the proportion of traffic covered by national roaming, thereby affecting the blended unit costs for each type of traffic. Usage multipliers: determine the mark-ups that should be applied to current year traffic usage in order to reflect future year traffic usage on 2G/3G networks in Norway. Year-on-year trends for retail prices (in real terms): determine the retail prices for the future year based on the current prices assumed. Termination rate inputs: specify the values of the average fixed and mobile termination rates in future years. Year-on-year trends for wholesale prices of voice/sms/data: determine the wholesale prices for the future year (in real terms) based on the current prices assumed. The margin model has been adapted to calculate the margins for products in 2011 and 2020 (both expressed in real 2011 NOK currency). An alternative future year can be calculated, but this would require some specific inputs in the model to be updated for that given year, using the LRIC model. The margin model can also assume that, for a given population coverage, that there is no national roaming.

60 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 57 The inputs in the model that have been revised for the 2020 calculation are summarised in Figure 6.1, along with the original 2011 value. Inputs derived using the LRIC model are highlighted in bold. Figure 6.1: Summary of inputs revised for a calculation in 2020 [Source: Analysys Mason, 2012] Input description 2011 value 2020 value Network coverage as a proportion of traffic (Option 1) 53.30% 70.50% Network coverage as a proportion of traffic (Option 2) 58.40% 87.10% Network coverage as a proportion of traffic (Option 3) 59.90% 90.50% Network coverage as a proportion of traffic (Option 4) 62.40% 95.80% Network coverage as a proportion of traffic (Option 5) 62.10% 99.75% Voice usage per subscriber as a proportion of 2011 voice usage SMS usage per subscriber as a proportion of 2011 SMS usage Data usage per subscriber as a proportion of 2011 data usage Retail prices for monthly fees and out-of-bundle traffic as a proportion of 2011 price * Average mobile termination rate across all mobile voice termination NOK 0.37 NOK 0.15 Average fixed termination rate across all fixed voice termination NOK 0.05 NOK 0.05 Voice/SMS wholesale prices as a proportion of 2011 prices * Data wholesale prices as a proportion of 2011 prices * Unit network costs of voice, SMS and data Derived with LRIC model * These values are modelled based on a 2% year-on-year decline. In order to generate the data for the last entry (which is stored on the Network costs worksheet), the LRIC model must be run in a particular configuration. To calculate the unit network costs in 2011 and 2020, the third operator calculation is run using zero weighted average cost of capital (WACC) and with the assumptions of 3G infill coverage and 3G 2100MHz+900MHz area coverage from the v7.1 LRIC model restored. We then calculate the blended 2G/3G unit costs of on-net voice, off-net voice, on-net SMS, off-net SMS and high-speed data megabytes. These costs are expressed in real 2011 NOK and include mark-ups for network common costs, location updates and business overheads. For the three cases of population coverage over 90% in 2020 set out in Figure 6.1, we modify the coverage of the national configuration in the LRIC model. For the two cases under 90% in 2020 in Figure 6.1, we modify the coverage of a sub-national configuration Margin calculations for a mobile-broadband-only subscription We have added a mobile broadband user product into the margin model, allowing for the calculation of the margins for such a product. The product has been added alongside the four products already modelled (Prat, Komplett, Bedrift and Posten Norge). It primarily allows a basket of products to be tested overall, reflective of an operator such as Tele2 and Network Norway which serve a cross-section of users, including average-usage mobile broadband subscribers.

61 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 58 We have based our subscriber and long-term forecasts assumptions on those used for the v7.1a LRIC model forecasts. Our assumed average revenue per megabyte is calculated from NPT s market statistics, based on the market-wide revenue per megabyte averaged across data for first-half 2010, second-half 2010 and first-half These assumptions, which are summarised below, can be found on the Control worksheet. Inputs derived using the LRIC model are highlighted in bold. When a subscriber is served using national roaming, the margin model purchases the total national roaming capacity according to, with the wholesale price decline (as a proportion of 2011 prices) applied from Figure 6.1 above in order to derive an assumed wholesale cost in Figure 6.2: Summary of inputs used for the modelled mobile-broadband-only product [Source: Analysys Mason, 2012] Input description 2011 value 2020 value Mobile-broadband-only subscribers as a proportion of total 11.2% 17.8% Assumed usage in megabytes per subscriber per month Assumed revenue per megabyte, excluding VAT (NOK) * Megabytes included in bundle Proportion of clients exceeding their subscription 0% 0% * This is modelled based on a 2% year-on-year decline. These mobile-broadband-only assumptions effectively correspond to a product where the operator receives, on average, NOK100 per subscriber per month in 2020, and each subscriber consumes, on average, 1250 megabytes per month in total (both downlink and uplink). The margin model can test other retail data-only product structures. We note that such structures (e.g. with additional charges for out-of-bundle megabytes) can influence the resulting margins to quite a significant extent. However, we have limited our investigation to just one example for a simple projection of average retail revenue and average consumption. The wholesale costs of national roaming for this product are based on Telenor s current standard wholesale offer. With regard to expected future usage, we note that an average usage of 1250 megabytes per month is consistent with the assumed long-term forecast for high-speed data usage from the v7.1 (and v7.1a) LRIC model. We have also tested more aggressive assumptions for mobile-broadband-only usage (specifically, with the usage doubled), in order to establish the impact of such increased usage on overall margins. 6.2 Calculation of average margins In this section, we illustrate how we have used the refinements to the margin model described above to investigate overall average margins for earnings before interest and taxes (EBIT) in three different cases:

62 EBIT margin for the four original products only EBIT margin for all five modelled products Investigations into the costs, coverage, capacity and margins of mobile networks in Norway 59 Lower megabytes per subscriber, lower penetration : mobile-broadband-only subscriptions make up 11.2% of all subscriptions, and their data usage per subscriber per month is 1250 megabytes. Lower megabytes per subscriber, higher penetration : mobile-broadband-only subscriptions are 17.8% of all subscriptions, and their data usage per subscriber per month is 1250 megabytes. This is equivalent to the 2020 usage in the v7.1a LRIC model. Higher megabytes per subscriber, higher penetration : mobile-broadband-only subscriptions are 17.8% of all subscriptions, and their data usage per subscriber per month is 2500 megabytes. In each case, we calculate the average EBIT margin in 2020 under the five options for 2020 population coverage (ranging from 70.50% to 99.75%, as set out in Figure 6.1 above). We calculate these values of EBIT for two situations: (a) a basket of four handset-user profiles (consisting of the four products originally considered within the margin model); and (b) a basket of five products (i.e. including the mobile-broadband-only product). These situations are shown in Figure 6.3 and Figure 6.4 respectively. For the first situation, we show only the result for the Lower megabytes per subscriber, higher penetration case: the other two cases lead to average EBIT margins that are not more than 3% higher than this case. For 99.75% coverage, we calculate the average EBIT margin both with and without national roaming costs (i.e. where the operator is assumed to still be using some national roaming, or to no longer use roaming). Figure 6.3: Average EBIT margin in 2020 across the original basket of four products [Source: NPT margin model, 2012] Figure 6.4: Average EBIT margin in 2020 across the basket of all five products under different coverage options [Source: NPT margin model, 2012] Lower MB, lower pen Higher MB, higher pen Lower MB, higher pen We note that the charts above are based on. These charts could change if a different pricing structure, either current or future, was assumed.. In this context, we observe in Figure 6.4 that the increase in margin on voice-focused products achieved by increased coverage is relatively small beyond 75% coverage, and in fact declines beyond 87% coverage until national roaming costs are removed. This decline in margin is more pronounced when data-focused products are also included within the average EBIT calculation.

63 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway Conclusions We observe that operators are currently upgrading the installed speeds in their networks 27, and secondly that they currently offer products with high allowances 28, meaning that the envisaged average usage could be assumed to be above even our higher megabytes per subscriber. However, the availability of such options certainly does not imply that subscribers will be mainly purchasing only this product and consuming the full allowance. In fact, average usage may be diluted by consumers buying the cheaper products with smaller allowances. It is unlikely that operators will be able to achieve more (or even the same) revenue per megabyte from higher usage in the future, meaning that the average revenue per megabyte is likely to be lower (as we have reflected in Figure 6.2 above). Therefore, higher usage may lead to lower revenue per megabyte, which would most likely result in a lower average EBIT across both voice-focused and data-only products. Therefore, we believe that our cases above are a reasonable and conservative view of the possible margins achievable. When we consider the average EBIT margins across a basket of the five products and assuming (having also included a mobile-broadband-only product), it can be seen that the level of data usage has a key role to play. Even if more aggressive data usage assumptions are used, average EBIT margin is fairly constant between 70% and 87% coverage, but falls more significantly as coverage rises beyond 87% (falling by % to % in EBIT terms). It is only with the removal of national roaming costs altogether at 99.75% coverage that we see an increase in this EBIT margin (by approximately % in EBIT terms) For example, see a recent announcement by NetCom at /journal_content/56_instance_w4vy/10156/ For example, see where Telenor offer products with an allowance of up to 20GB.

64 Investigations into the costs, coverage, capacity and margins of mobile networks in Norway A 1 Annex A Confidential coverage calculations

65 Total megabytes(billions) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway B 1 Annex B Sensitivity of the TMEC curve to data usage We note that the v7.1 LRIC model has relatively conservative forecasts of high-speed data usage. The demand updates that we undertook for Section 3.1 whilst deriving v7.1a retained this principle of relatively conservative data forecasts. We recognise that there is scope for more aggressive forecasts to be considered (as we have in some of the analyses in the main body of our report), which we have undertaken as sensitivity on the TMEC curve tests for the purposes of this annex. We have doubled the average high-speed data usage per subscriber per month from 1000MB to 2000MB in the v7.1a model. This increases the modelled total data (low-speed and high-speed) across Telenor, NetCom and the third operator to be 40 billion megabytes in the long term, as shown below in Figure B Figure B.1: Comparison of total modelled data megabytes; the dotted line indicates the number of megabytes that are low-speed [Source: NPT LRIC models, 2012] v7.1a model (increased) v7.1a model v7.1 model We have re-run this updated version of the model in order to generate the TMEC curve for Coverage profile A, as described in Section A comparison of the two TMEC curves is shown below. The minimum point of each curve is illustrated by a dashed line of the same colour. As can be seen, the shape of the curve is unchanged, and there is no sideways shift in the population coverage of the curve s minimum value. This is because the shape of the curve is driven by changes in the traffic-sensitive assets in the networks of the three operators. The large capacity of the 3G networks deployed means that the network design is not significantly affected by the higher 3G data throughput that is assumed, although there is a higher utilisation of the fixed costs of the three 3G high-speed data networks.

66 Total mobile economic cost (billions) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway B Figure B.2: Comparison of the TMEC curves plotted assuming 1000MB and 2000MB of high-speed data per subscriber per month in the long term [Source: Analysys Mason, 2012] Total 3G population coverage original, 1000MB original, 2000MB We observed that the capacity of the HSPA network overlay in the LRIC model for the third operator was sometimes insufficient for the traffic it was assumed to convey with the increased data traffic forecast. We therefore ran another sensitivity whereby HSDPA was deployed at all NodeBs from 2011 onwards 30. This increased the modelled data capacity significantly, meaning the network could support the data traffic it was assumed to carry in practically all cases. The results of this further sensitivity are shown below. We calculate the TMEC curves in four cases; the original network design at both levels of data demand (pink and purple lines), and the adjusted network design at both levels of demand (dark blue and light blue lines). For consistency, we assume that all three modelled radio network operators uses HSDPA14.4 across their entire 3G network from 2011 onwards. There is a large increase in the TMEC across the whole curve. However the shape of the curves again remains more or less the same. This due to the increased network costs of the larger, faster HSPA network overlay for all three 3G networks, which is essentially a (significant) extra fixed cost of coverage for each operator. In this particular case, the minimum of the two curves under the revised network design (dark blue and light blue dotted lines) is lower by a small percentage (up to 5%), but we do not consider this to be significant given the shallow shape of the curve. A particularly relevant conclusion of this analysis is that higher data traffic does not lead to a significantly higher third operator population coverage minimum TMEC This is the highest grade of HSDPA available within the model following the upgrade completed in This did require some formula adjustments to the HSPA network design in the LRIC model.

67 Total mobile economic cost (billions) Total mobile economic cost (billions) Investigations into the costs, coverage, capacity and margins of mobile networks in Norway B Figure B.3: Comparison of the TMEC curves plotted assuming 1000MB and 2000MB of high-speed data per subscriber per month in the long term; before and after corrections [Source: Analysys Mason, 2012] 130 Total 3G population coverage original, 1000MB refined, 1000MB original, 2000MB refined, 2000MB We have also tested this revised level of high-speed demand, and the adjusted HSPA network design, according to the different national roaming assumptions that we used in Section 3.3. Figure B.4 below shows the output of this calculation, which can be compared with Figure When making this comparison, it can be seen that the curves have very similar shapes and indicate a very similar range of minima in both charts Figure B.4: TMEC curve for coverage profiles within Coverage profile A, using the higher demand and adjusted network design, under different national roaming assumptions [Source: Analysys Mason, 2012] Total 3G population coverage 0% on Telenor 50% on Telenor 100% on Telenor Based on these analyses, our conclusion is that increasing the 3G high-speed data usage in the model from the levels in the v7.1a model (and also increasing the deployed data capacity of the 3G network in the model for consistency reasons) does not change the shape of the TMEC curve. Furthermore, it would not lead to a change in our conclusions on population coverage for the minimum TMEC. Therefore, we believe that the high-speed usage assumptions used in Section 3 of our report are sufficient for the purposes of this report.