www.greenpacket.com
WHITEPAPER
Operators Can Save $14 Million Yearly
Through Data Offloading
A TCO Study & Calculation on ...
Abstract
Of late, network congestion is one of the most talked about topic in the telecoms industry has is
attributed to t...
Contents
Can Somebody Define Network Congestion? 01
Where Network Congestion Occurs? 04
Network Upgrade: Total Cost of Owne...
01
Can Somebody Define Network Congestion?
Network congestion is at the top of everyone’s mind in the telecommunications i...
02
As such, Operators need to continuously re-design and optimize their infrastructure to handle different traffic patterns...
03
Hence, depending on the intended bandwidth operators wish to extend to their subscribers, the network deployment
has to...
04
Where Network Congestion Occurs?
To help understand where network congestion occurs, let’s examine a typical HSxPA netw...
05
Transmission (Backhaul) Congestion
Tranmission (Point B as shown in Figure 1) or sometimes referred to as backhaul play...
06
The routing of voice using Adaptive Multi-Rate (AMR) flows from Iub to Iu-CS, accessing the Media Gateway (MGW/MSC)
and ...
07
During the early stages of network planning, the task of forecasting CAPEX on Node B based on the number of sites is
st...
08
WHITEPAPER
A base station upgrade generally involves several areas – channel element, code, power, and multi carrier as...
09
When code congestion occurs, a typical HSDPA solution is to increase the speed from 3.6Mbps to 7.2Mbps or
14.4Mbps (or ...
10
WHITEPAPER
Channel Element (CE)
While code, power and carrier are similar among infrastructure vendors, channel element...
11
Network Upgrade: Total Cost of Ownership (TCO)
Breakdown
The earlier section explored network improvement mechanisms su...
12
Source: In-Stat, June 08
Figure 8: Network TCO, outlining CAPEX and OPEX
Is There A Cheaper Alternative?
Though the gro...
13
WHITEPAPER
With its Seamless Mobility advantage, ICMP doubles up as a
cost-effective, hassle-free and immediate data of...
14
Source: Greenpacket
Figure 11: TCO breakdown of an Asia Pacific 3G Operator
Network Dimensioning
In this study, the fol...
15
Source: Greenpacket
Figure 12: Network factors considered by Greenpacket for data offloading calculation
Operator’s Net...
16
Figure 14: Network evolution of the selected operator
Network Scale and Node B Distribution
Figure 15: Distribution of ...
17
Population Breakdown
Figure 17: Breakdown of population in dense urban, urban and rural areas
Subscriber Profile
Curren...
18
WHITEPAPER
Dense Urban
Urban
Rural
53%
35%
12%
WiFi Network
Figure 20: WiFi networks in dense urban, urban and rural ar...
19
5. E1 is used to provide 3.6Mbps; STM-1 for 7.2Mbps, FE for 14.4Mbps and 21.1Mbps.
6. All Node B’s can support 2 IMA gr...
20
Cost (OPEX) Savings
IUB (Transmission) Savings
In a five-year period and using Greenpacket’s ICMP to facilitate data offl...
21
Total Savings
WHITEPAPER
Total Savings
Savings of 16% (of OPEX) or 2.9% (of TCO)
With a operational expenditure of USD ...
22
WHITEPAPER
Find Out How Much You Can Save Through Data Offloading!
Greenpacket welcomes you to embark on the offloading ...
23
References
1. Telecoms: At the starting line – The race to mobile broadband by Gareth Jenkins and Jussi Uskola, Deutsch...
About Green Packet
Greenpacket is the international arm of the Green Packet Berhad group of companies which is listed on t...
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OPERATORS CAN SAVE $14 MILLION YEARLY THROUGH DATA OFFLOADING

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Of late, network congestion is one of the most talked about topic in the telecoms industry has is attributed to the overwhelming growth in data consumption. There is an assortment of solutions to combat congestion, ranging from high investment to cost-effective and short-term to long-term. In this paper, Greenpacket puts forth a cost-effective, immediate and long-term solution to network congestion – data offloading.

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OPERATORS CAN SAVE $14 MILLION YEARLY THROUGH DATA OFFLOADING

  1. 1. www.greenpacket.com WHITEPAPER Operators Can Save $14 Million Yearly Through Data Offloading A TCO Study & Calculation on Data Offloading
  2. 2. Abstract Of late, network congestion is one of the most talked about topic in the telecoms industry has is attributed to the overwhelming growth in data consumption. According to Cisco, all around the world, mobile data traffic is expected to double every year through 2014. With such massive demands for data, industry stakeholders are looking at various measures to cope with the increase and mitigate congestion issues. There is an assortment of solutions to combat congestion, ranging from high investment to cost-effective and short-term to long-term. In this paper, Greenpacket puts forth a cost-effective, immediate and long-term solution to network congestion – data offloading. We examine a typical cellular operator’s network structure, congestion points and total cost of ownership (TCO) and next, outline a calculation model (based on an Asia Pacific cellular operator) to demonstrate how much operators can save by offloading data to a secondary network such as WiFi. Data offloading directly impacts 36.5% of a network’s TCO. As such, operators can potentially* save USD 14.4 million/year or USD 72 million over 5 years through data offloading. *Cost savings suggested in this paper are based on a network of 7,000 Node B’s. WHITEPAPER
  3. 3. Contents Can Somebody Define Network Congestion? 01 Where Network Congestion Occurs? 04 Network Upgrade: Total Cost of Ownership (TCO) Breakdown 11 Data Offloading: TCO Study and Calculation 13 Cost (OPEX) Savings 20 Find Out How Much You Can Save Through Data Offloading! 22 WHITEPAPER
  4. 4. 01 Can Somebody Define Network Congestion? Network congestion is at the top of everyone’s mind in the telecommunications industry as it impacts stakeholders in different ways. Operators fear it, users complain about it, governing bodies hold meetings over it, while telecom vendors introduce new solutions to deal with it. On the contrary, infrastructure vendors cannot get any happier as network congestion provides the dais for increasing revenue. With so much drones over this issue, can anyone define network congestion? How does one benchmark a network to be congested Industry experts relate network congestion to the increase in global data consumption which will rise 100-fold over the next four years! Meanwhile, some industry groups blame the proliferation of mobile broadband devices such as smartphones and embedded devices, while some say that unlimited data business models are the cause. While data consumption increases exponentially, it is also fair to relate this increase to the tremendous adoption of broadband among users over the past three years. In simple math, more users lead to more data usage. Of course there is no doubt that users use more data today also thanks to buffet pricing plans and mobile devices that enable access to data anytime, anywhere. However, this does not give a clear picture of network congestion. Can it be attributed to the number of subscribers operators have? Probably not, instead, it drills down to the efficiency of network planning. For example, Operator X with 100,000 subscribers running on a 21.1 HSPA+ network built from 10,000 base stations may not face network congestion as opposed to Operator Y with 50,000 subscribers on a 3.6Mbps HSDPA network built from 10,000 base stations. Aside from network planning, user profiles play a vital role as well. How much data traffic deteriorates the network quality and upsets a user? Does a user on 256kbps speed have the case to declare a network as congested just because video streaming is slow? Would complaints be justified when the user’s neighbor, also a subscriber to the same network, enjoys uninterrupted instant messaging sessions with his girlfriend overseas? While network congestion is very much related to a network with high traffic loads but limited bandwidth capacity, it ultimately boils down to user expectations. One user might define minimum broadband speeds to be at 256Kbps while another sets it at 2Mbps. Network Planning – When Coverage Compensates Capacity The task of network planning can never be too precise or complete. For a Greenfield operator, network planning can be as simple as focusing on coverage and establishing network sites in areas with large population – the number of cells and base stations required for the area can be easily defined just by considering the propagation model and path loss. However, it gets complicated when the network matures and capacity becomes an issue rather than coverage. At this point, the network load exceeds capacity level thus requiring additional cells and network sites to be added. There are many factors that can affect a network’s stability and this phenomenon cannot be forecasted for preventive action. Network deployments in areas with ongoing development can suddenly face congestion. For example, a new high density residential project or university can cause a radical change in population, leading to higher consumption of bandwidth and result in congested networks. WHITEPAPER
  5. 5. 02 As such, Operators need to continuously re-design and optimize their infrastructure to handle different traffic patterns – for example a college area would generate high traffic as gaming, video streaming and social networking are associated with students’ lifestyle. On the contrary, an industrial area demands less traffic as the internet would be used primarily for email correspondence and web browsing. Network Planning – Reverse Engineering Network planning is not as easy as building one site for every 1km radius. A rural area of 10km2 may only require three sites, but on the contrary, a dense urban area might demand 30 sites. Meanwhile, the site requirements can differ even for urban areas with similar number of users. Let’s assume that there are two different sites – one a university and the other a residential area, both 3km apart and have 100 active subscribers. The traffic in the university area could be higher by 10-fold as compared to the residential area due to different types of internet activities that contribute to the levels of network congestion. To overcome this problem, an operator might try to increase the number of sites surrounding the university. Yet, bandwidth will be consumed thoroughly and subscribers will remain unsatisfied. Hence, how many sites would be enough? There is never a perfect solution in network planning. What matters is to deliver a throughput level justifiable to subscribers and a data rate which is sufficient to satisfy subscriber usage. To conduct network planning through reverse engineering, an operator would need to embark on the following: 1. Understand the population demographics and internet usage patterns. 2. Decide on the intended throughput per user. 3. Based on projected subscriber base and intended throughput per user, the operator has to work backwards to determine the number of sites and infrastructure capacity required. Intended throughput per user is not a straight-forward figure and is subject to environmental conditions and interference. The following table outlines the average throughput a user would gain (intended throughput) according to different network capacities. *Estimated to be about 60% of theoretical speed in view of environmental conditions and interference that affects network speed. **Infrastructure vendors define a range of 48-64 users/cell as bottleneck of an HSxPA base station. WHITEPAPER 3.6Mbps 7.2Mbps 14.4Mbps 21.1Mbps 28.8Mbps 2.16Mbps 4.32Mbps 8.64Mbps 12.66Mbps 17.28Mbps 60** 36Mbps 72Mbps 144Mbps 211Mbps 288Mbps HSDPA HSPA+ Average Throughput/User (Intended Speed) Maximum Users/cell Actual Speed* per cell Theoretical Speed per cell
  6. 6. 03 Hence, depending on the intended bandwidth operators wish to extend to their subscribers, the network deployment has to be planned accordingly. For example, if an operator intends to offer a bandwidth of 256Kbps/user, a HSPA+ 21.1Mbps site has to be deployed (on assumption that the cell hosts a maximum capacity of 60 users). Alternatively, i. Operators can reduce the forecast of intended active users/cell to 30 and ii. Double the number of cells to cater for that traffic or iii. Increase the number of sectors per base station for similar throughput. Theoretically, this means that the operator can deploy either method: a. HSPA+ 21.1Mbps via S1/1/1 b. HSPA 14.4Mbps via S2/2/2 c. HSDPA 7.2Mbps via S2/2/2/2/2/2 WHITEPAPER
  7. 7. 04 Where Network Congestion Occurs? To help understand where network congestion occurs, let’s examine a typical HSxPA network as shown in Figure 1. A HSPA network is often divided into two parts– Radio Access Network (RAN) and Core Network (CN) and each level within has varying bandwidth capabilities. Congestion can occur at anywhere from RAN (RNC, Node B) to CN (from SGSN to GGSN), as well as at all transmission points connecting each access point. Today’s CN is able to support high capacities of between 10-40Gbps while RNC is able to take up 2-8Gbps (depending on infrastructure vendors) and Node B (30-50Mbps). In saying this, any throughput will never be enough to cater to the demands of users. Bottleneck can occur anywhere within the network, but more often happens at the RAN (specifically on the Node B) level. Transmission is another congestion prone area and this is a concern as approximately 25-30% of base stations in the world are using E1/T1 (this is further explained in the section below, Transmission (Backhaul) Congestion). Hence this paper focuses on congestion at RAN, particularly Transmission (Backhaul) and Node B, and how to ease congestion at this level. Source: Greenpacket Figure 1: A typical HSxPA network diagram WHITEPAPER RAN CN HLR/AUC SMS SCE SCP BG GGSNSGSN RNC RNC CG MSC/VLR GMSC Node B E1 Node B E1 Node B E1 Node B E1 Node B E1 Node B E1 Node B E1 Node B E1 Node B E1 Node B E1 Node B E1 Node B E1 PSTN ISDN SS7 Internet, Intranet Other PLMN GPRS backbone
  8. 8. 05 Transmission (Backhaul) Congestion Tranmission (Point B as shown in Figure 1) or sometimes referred to as backhaul plays an important role in transporting data packets from one point to another. However, it is limited in terms of total bandwidth it can support and is often the area of worry for telecoms network specialists. In a study conducted by Ovum, respondents said that transmission (backhaul) poses a pressing concern and places a restraint on mobile services (Figure 2). Source: Ovum, South East Asia COM Conference, July 2010 Figure 2: Respondents’ thoughts on backhaul capacity Figure 3: Simplified network diagram of a HSxPA network with emphasis on Transmission Figure 3 depicts a simplified HSxPA network diagram emphasizing transmission paths. A typical transmission can appear more complicated than shown here (possible looping from one Node B to another in a star, tree or ring topology, conversion from TDM to IP, going through aggregation points or hub base station). However, for the purpose of examining congestion at transmission level, we will consider transmission from an interface point of view, encompassing Iub, Iur, Iu-CS and Iu-PS. WHITEPAPER Currently a restraint on mobile services Will be a restraint on mobile services in the next 12 months Won't be a restraint on mobile services for the foreseeable future Don't know 34% 17% 33% 16% Do you think backhaul capacity is... SGSN MSC Core Network RNC Node B Node B Iub Iu-PS Iur Iu-CS Iub RNS RNC Node B Node B Iub Iub RNS
  9. 9. 06 The routing of voice using Adaptive Multi-Rate (AMR) flows from Iub to Iu-CS, accessing the Media Gateway (MGW/MSC) and possibly terminates at a PSTN or another mobile network. Since voice service is measured at 12.2kbps and does not consume much bandwidth (in comparison to data), we can easily discard the routing of lu-CS in this TCO calculation. The primary concern is focused on data that routes from Iub, Iu-PS and possibly Iur. While data travels predominantly on the Iu-PS interface, most Iu-PS channels today are equipped with STM-1, STM-4 or FE/GE which are well able to support the capacity of hundreds of Mbps. Unfortunately, this is not the case with Iub as a significant number of Node B’s today still uses E1 or T1 (in US) and STM-1, whereas less than 5% of operators have migrated to a full FE configuration. E1/T1 channels emerge as bottlenecks when the HSPA network grows from 3.6Mbps to 14.4Mbps onwards, resulting in congestion issues. Transmission Cost It is common for a HSxPA operator to initially embark deployment using E1/T1 with a 2Mbps/line. In rural areas, two to three E1s are needed in a 3.6Mbps per cell, three cell configuration site. On the other hand, an urban location with a similar cell setup would require four to five E1s per site. As the network matures with more active users, operators are required to add more E1/T1 of their own or rent them. Transmission rental differs significantly from one country to another and normally can consume as much as 20-30% of total cost of ownership. Today, base stations support a maximum of 8E1 IMA, which has a capacity of 16Mbps. If this is insufficient, an upgrade to fiber transmission (STM-1) is necessary. As the network gets upgraded to HSPA+ network using IP, operators may then need to convert their Iub transmission to Ethernet (FE/GE) as similar approach done by operators such as Etisalat, E-Mobile and Starhub. Node B (RAN) Congestion In the same research conducted by Ovum on radio access network (RAN) capacity, respondents also believe that RAN is also a roadblock. 64% believe that RAN is currently or will put a constraint on mobile services over the next 12 months, as shown in Figure 4 below. Source: Ovum, South East Asia COM Conference, July 2010 Figure 4: Respondents’ thoughts on RAN capacity WHITEPAPER Currently a restraint on mobile services Will be a restraint on mobile services in the next 12 months Won't be a restraint on mobile services for the foreseeable future Don't know 36% 15% 28% 21% Do you think radio access network capacity is...
  10. 10. 07 During the early stages of network planning, the task of forecasting CAPEX on Node B based on the number of sites is straightforward. However, the actual cost of Node B does not end here, instead it will undergo constant upgrades and over the next 5 years, the cost spent on upgrades might exceed the cost of purchasing the Node B itself. The prime reasons for these upgrades are contributed by an increase in capacity requirements and in some extreme situations, congestion. When does a Node B experience congestion and demand an upgrade? Network upgrades can be conducted using two methods: i. Base station capacity upgrade (involves channel element, power transmit, multi-carrier and HSPA codes) ii. Network upgrade (by increasing sites) Method #1 - Base Station Capacity Upgrade When it comes to network improvement, a more cost-effective alternative for operators is to upgrade their existing base stations in terms of throughput per cell, for example from 3.6Mbps to 7.2Mbps or 14.4Mbps. How does this work? Let’s assume that Operator A launches a HSPA network with three cells, each with a throughput of 3.6Mbps as shown in Figure 5. Due to environmental constraints and inteference between users, Greenpacket estimates that the average throughput per cell is at 60% of the theoretical value i.e. 2.16Mbps. During peak hours with 10 active users, each user gets approximately 220kbps speed. However, as subscribers grow to 20 active users, each user will only obtain a mean speed of 100kbps. It is important to note that a HSPA network can support 48-64 users per cell – as the number of users per cell increase, average speed per user decreases and this calls for an upgrade. Figure 5: HSDPA S/1/1/1 Network Site WHITEPAPER Assuming this is a HSDPA S1/1/1 network site Bandwidth capacity = 3.6 Mbps (practically, ~ 2 Mbps/sector) Planned subscribers/sector = 10 Actual subscribers/sector = 20 Result = Congestion Node B 3.6Mbps 3.6Mbps 3.6Mbps
  11. 11. 08 WHITEPAPER A base station upgrade generally involves several areas – channel element, code, power, and multi carrier as shown in Figure 6. Figure 6: RAN upgrade involving Node B Transmission Code Figure 7 shows the Orthogonal Variable Spreading Factor (OVSF) code tree. At SF=16, 15 HS=PDSCH codes can be used for HSDPA purposes. As HS-PDSCH codes can range from 1 to 15, the remaining codes will be utilized by R99 and AMR. Different applications will accept different spreading, for example for voice AMR, the codes can be further spread to SF=256. Figure 7: Orthogonal Variable Spreading Factor (OVSR) code tree NODE B Channel Element (CE) Code Power New Site Carrier Iub Congestion Transmission AMR 12.2kbps 15 HS-PDSCH Codes SF = 1 SF = 2 SF = 4 SF = 8 SF = 16 SF = 32 SF = 64 SF = 128 SF = 256 X - blocked by lower code in tree
  12. 12. 09 When code congestion occurs, a typical HSDPA solution is to increase the speed from 3.6Mbps to 7.2Mbps or 14.4Mbps (or in other words increase the HSDPA codes). Table below shows the corresponding code to speed. *Today, all HSPA Node B’s support 16QAM modulation. HSPA+ requires 64QAM modulation. Table 1: Correspoding code rates to speed Note: Adding codes come at a price as a trade off of lesser codes occurs for R99 and AMR. This will create a problem in locations where voice and R99 are still dominant, leading to other congestion issues. Code upgrades are purely done via software licenses from infrastructure vendors, with typical license prices based on five codes per base station. Multi Carrier Solving code congestion may lead to congestion on the carrier level. With more codes dedicated to HSDPA, there will be lesser codes available for R99. Instead of allowing the trade off, a popular strategy for operators is to add an additional carrier per cell (from S1/1/1 to S2/2/2 of S3/3/3). This carrier overlaying strategy means that technically each cell can have up to 15 + 15 codes for HSDPA and R99. Depending on the operator’s deployment strategy, they may use both cells for HSPA (each with 10 codes) or employ 15 codes on the first carrier, while the second carrier is used solely for R99. Carrier upgrading mainly involves software, however sometimes hardware changes are required depending on limitations on the base station. Older versions of base stations support transmit receive unit (TRU) modules, where each TRU only holds a single carrier. Today’s technology allows multiple TRUs to be embedded within a single module, which is also known as multi radio unit (MRU). Each MRU consists of multiple power amplifiers (PA) that can support up to two or sometimes even four or six carriers per hardware module. Power Once code and carrier congestion are resolved, operators might face insufficient power problems. As more users are allowed to to connect to a single cell, each cell would then need more power to transmit and overcome interference. As coverage and capacity are co-related and often compensates one another, the natural outcome will be a shrinking cell coverage. Users at the cell edge will need more power, leading to insufficient power at the base station. Depending on the MRU power transmit capacity, operators may choose to use the power allocation differently. For example, with a MRU of 2 PA capability and maximum power of 40W per MRU, an operator may opt to transmit at 20W + 20W to cater for two carriers per cell. This may not be applicable to another operator who prefers to transmit at 40W per cell to achieve a further cell edge. Therefore, two MRU modules are required. Infrastructure vendors charge for upgrades in terms of MRU boards and a possible license fee to operate the carrier splitting. WHITEPAPER 1.8Mbps 3.6Mbps 3.6Mbps 7.2Mbps 5.4Mbps 14.4Mbps (Based on coding rate of 4/4) QPSK 16QAM Throughput with 15 codesThroughput with 10 codesThroughput with 5 codesModulation
  13. 13. 10 WHITEPAPER Channel Element (CE) While code, power and carrier are similar among infrastructure vendors, channel element (CE) deployment differs significantly. In general, one CE is used for one AMR 12.2kbps user. However, this may not be applicable for R99 and HSPA usage. Due to CE’s proprietary technology, some vendors may require eight and 16 CEs for PS144 and PS384, while another may need four to eight CEs.This applies to HSDPA and HSUPA where some vendors may need CE for every user while others may not. Because of this, the price of CE may vary between vendors to offset differences in the number of CEs supplied. When subscriber base increases in an area, voice and R99 may increase as well, leading to higher demand for CE from operators as well as CE congestion if not handled properly. Channel element is software supported by the base station's baseband and it can be upgraded up to the maximum level allowed by the hardware. The vicious cycle of network congestion may not take place in the above-mentioned order as subscriber usage habits differ. An example situation iswhereby power insufficiency due to cell edge may be resolved by adding more MRU, without increasing codes or CE. Similarly, additional five to 10 codes may be sufficient without adding carriers. Though most operators would prefer to upgrade the base station as it is fast, the cost of upgrading may not be justified when compared to the TCO. It could be cheaper to purchase a base station with higher capacity and more advanced configuration. Network planning is not easy,but done as accurately as possible, it could save an operator millions. Method #2 – Network Upgrade (By Increasing Sites) While base station upgrade remains the quickest option in terms of deployment, there is a limitation to the amount of upgrades. Sometimes a base station can only hold a maximum of six carriers and subsequently any additional carrier requires a new base station. Similarly, in situations where CE demands exceed the base station’s baseband configuration, an additional base station is required. Another advantage of upgrading sites is its long-term positive impact on the network. For example, adding more power to support cell edge users will not yield similar performanceas opposed to adding a new site at the cell edge or within the vicinity. Apart from better performance, operators need to compare the cost of upgrading versus the cost of adding a new base station. Though both their effect on the network may be similar, a newer base station requires lower maintenance and provides a full range warranty period. The disadvantage to a new base station,however, is that new site acquisition is needed and this could be a long process.
  14. 14. 11 Network Upgrade: Total Cost of Ownership (TCO) Breakdown The earlier section explored network improvement mechanisms such as base station upgrades and the addition of new sites which were not considered during the initial network planning stage. How much do network improvements contribute to the total network cost over a long period of time, say five years? First, a network’s total cost or TCO has to be understood. A network’s total cost comprises of both the capital expenditure (CAPEX) and operation expenditure (OPEX). The cost of a network does not stop just after it is rolled out. Instead, it is actually the beginning of many reoccurring costs such as maintenance cost, upgrade cost, site and bandwidth rental, manpower, power supply and others which fall under operations cost (OPEX). Most operators are concerned about CAPEX but fail to realize that in the long run (for example, five years), more is spent on OPEX. Moreover, OPEX costs such as manpower and electricity are always increasing , but CAPEX costs decreases as prices of infrastructure equipment usually declines as its technology matures. Figure 8 gives an overview of network TCO according to In-Stat, where 27% is spent on CAPEX and 73% on OPEX. While the TCO shows a CAPEX to OPEX ratio (percentage) of 73:27, Greenpacket believes that the ratio will eventually change to approximately 80:20 due to the reasons mentioned earlier. Network TCO – The Components For operators, CAPEX constitutes the purchase of infrastructure and transmission equipment, as well as antenna and other supporting accessories, while deployment cost involves site acquisition, equipment installation and civil works. On the other hand, OPEX encompasses site rental, power consumption, leased line rental as well as software and hardware costs. Meanwhile, maintenance costs cover the network’s upkeep and manpower. It is interesting to note that leased line and site rental forms the largest chunk of network TCO with a combined total of 43.8%. Leased line refers to the rental of E1 (though some operators may opt to construct their own backhaul, making it a cost that falls under CAPEX) and site rental refers to the rental operators have to pay for all their sites. Both leased line and site rental expenditures are closely related to network congestion that requires upgrades. Operators usually fret about millions being spent on equipment, but in actual fact, this component is only 5.4% of the total network cost. WHITEPAPER
  15. 15. 12 Source: In-Stat, June 08 Figure 8: Network TCO, outlining CAPEX and OPEX Is There A Cheaper Alternative? Though the growth in data usage may seem to be a boon to many operators, its rapid growth can be detrimental to an operator’s bottomline due to its associated CAPEX and OPEX costs caused by network congestion. Therefore, operators must place together a strategy to combat network congestion. There are various congestion management methods available on the market, and this includes policy control, data traffic offload, infrastructure investment and network optimization2 . From these methods, data offloading is the most preferred as it presents a more immediate and cost-effective approach. This is supported by same study conducted by Ovum and Telecom Asia, whereby respondents were asked what is the most effective solution to deal with traffic growth besides upgrading network infrastructure and 41% favored data offloading, as shown in Figure 9 below. Source: Ovum/Telecom Asia Figure 9: Data offloading is the prefered choice for network congestion management 2 Bridgewater Systems WHITEPAPER Excluding installing more capacity, what is the most effective solution to deal with traffic growth? Wi-Fi and offloading traffic of the macro network Other traffic management techniques such as throttling and use of policy control New charging schemes (QoS, SLA, etc) Femto cells Others 21.8% 12.6% 5.7% 41.0% 18.9% NETWORK TCO CAPEX (27%) Purchasing (14%) Deployment (13%) Operations (60%) Maintainance (13%) OPEX (73%) Maintenance 11.0% Man Power 3.7% Equipment 5.4% Transmission 1.4% Equipment Accessory 5.4% Antenna 1.4% Site Rental 21.9% Power 7.3% Consumption Leased Line 21.9% Hardware & 7.3% Software Site Acquisition 2.7% Installation 2.7% Civil Works 8.1%
  16. 16. 13 WHITEPAPER With its Seamless Mobility advantage, ICMP doubles up as a cost-effective, hassle-free and immediate data offloading tool. Based on preset profiles, Operators can determine the priority of network connection corresponding to the surrounding environment. Hence, ICMP intelligently monitors the network environment - if it detects that a user is using data services on a cellular network (such as 3G) and if there is less congested alternative network (such as WiFi, WiMAX, DSL) available in the same vicinity, ICMP transfers the user from 3G to WiFi without interruption to connectivity. Data Offloading: TCO Study and Calculation Data Offloading Tool Data offloading is done via Greenpacket’s Intouch Connection Management Platform (ICMP), an easy-to-use, single-client connection management solution, innovatively conceptualized from Mobile IP technology. Figure 10: Greenpacket’s Intouch Connection Management Platform (ICMP) Components Impacted Through Data Offloading Network deployment to improve coverage is a continuous CAPEX. Greenpacket believes that data offloading has a direct impact on the OPEX (operations cost) which tantamounts to 36.5% of the total TCO. While it is not possible to totally eliminate this cost, operators can significantly reduce it through data offloading to WiFi networks. Data offloading has a direct impact on the following components of the OPEX TCO: i. Hardware and software upgrade – Since data is being offloaded, there will befewer users accessing the HSPA network. Therefore, network upgrades such as (but not limited to) channel element, power, carrier and codes are reduced. ii. Leased line – Operators often have to upgrade the backhaul especially for the Iub interface to add more E1 channels or migrate to STM-1 and FE/GE. By offloading, existing backhaul can be maintained or requires fewer upgrades. iii. Power consumption – When fewer users group on the HSPA network, lower power is required for tranmission. Eventually, the base station will consume less power. iv. Site rental – In situations where data is offloaded to WiFi networks, the number of sites can be minimized. This contributes to savings on site rental, civil works and CAPEX expenditure related to site acquisition.
  17. 17. 14 Source: Greenpacket Figure 11: TCO breakdown of an Asia Pacific 3G Operator Network Dimensioning In this study, the following areas are considered for costs calculation. Transmission will have an impact on Iub, Iu-PS and Iur, but to simplify the calculation, only Iub transmission savings will be considered. RAN upgrades will have an impact on both Node B and RNC, but again for handling simpler illustration, we will calculate Node B’s cost only. Our dimensioning tools were used to study an operator in Asia Pacific and these data were obtained: i. The operator’s network scale (migration path from HSPA to HSPA+) over the next 5 years ii. Traffic profiles such as user habits and peak hours iii. Total number of Node B’s expected over five years iv. Equipment vendor (as equipment dimensioning from one vendor to another differs)* From the dimensioning tools, traffic that will occur during peak hours and its cost over the next five years is generated. Monetary savings are then calculated comparing the traffic and costs against offloading to a WiFi network. *Name and details of infrastructure vendor withheld to protect its interests WHITEPAPER NETWORK TCO CAPEX (27%) Purchasing (14%) Deployment (13%) Operations (60%) Maintainance (13%) OPEX (73%) Maintenance 11.0% Man Power 3.7% Equipment 5.4% Transmission 1.4% Equipment Accessory 5.4% Antenna 1.4% Site Rental 21.9% Power 7.3% Consumption Leased Line 21.9% Hardware & 7.3% Software Site Acquisition 2.7% Installation 2.7% Civil Works 8.1% 0 20 40 60 80 100 ~14% ~13% ~60% ~13% Purchasing Deployment Operation Maintenance TOTAL Data offloading directly impacts 36.5% of TCO
  18. 18. 15 Source: Greenpacket Figure 12: Network factors considered by Greenpacket for data offloading calculation Operator’s Network Data In this section, we will examine the following input parameters used to perform the calculation. Source: Greenpacket Figure 13: Input parameters for data offloading calculation HSPA Evolution The selected cellular operator has a five year network evolution plan, moving from 3G (3.6Mbps) to HSPA (7.2Mbps) and eventually to HSPA+ as shown in Figure 14. WHITEPAPER Input Network Scale & Node B Distribution Traffic Profile WiFi Network HSPA Evolution Subscriber Profile Equipment Vendor Price of Upgrade Iu-CS Iu-PS PS Signaling PS Traffic CS Signalling CS Traffic Iur Iub CE Codes Carrier Power Assumptions Output RNC Transmission Output SGSN BG, DNS, DHCP, Firewall, Router... GGSN CG Node B MSC Server MGW HLR                 UTRAN CS CN PS CN Input Network Scale & Node B Distribution Traffic Profile WiFi NetworkHSPA Evolution Subscriber Profile Equipment Vendor Price of Upgrade Assumptions
  19. 19. 16 Figure 14: Network evolution of the selected operator Network Scale and Node B Distribution Figure 15: Distribution of sites by dense urban, urban and rural areas Traffic Profile Site Configuration Figure 16: Site configuration over 5 years WHITEPAPER 0 5000 4000 3000 2000 1000 6000 Dense Urban Urban Rural Total Sites 7000 2008 2009 2010 2011 2012 Initial Deployment Phase 1 Node B 3.6Mbps with priority on R99 (10 codes) HSPA Stage 7.2Mbps on Hotspots, migration to STM-1, 3.6Mbps on less congested area HSPA+ Stage Maintain old Node B to support HSPA, new Node B deploy on HSPA+ 3G (R99+HSPA) 7.2Mbps (R99 + HSPA on single carrier) Evolve to 14.4Mbps Dual Carrier HSPA+ 21Mbps CPC and CELLFACH 100% 0% 0% 0% 60% 40% 0% 0% 20% 80% 0% 0% 0% 20% 30% 50% 0% 0% 0% 100% HSDPA 3.6Mbps/cell Single Carrier HSDPA 7.2Mbps/cell Single Carrier HSDPA 14.4Mbps Dual Carrier HSPA+ 21Mbps Dual Carrier 2011201020092008Site Configuration 2012
  20. 20. 17 Population Breakdown Figure 17: Breakdown of population in dense urban, urban and rural areas Subscriber Profile Current and Projected 3G Active Subscribers Figure 18: Number of current and projected 3G active subscribers Network Usage Patterns Figure 19: Network usage patterns over 5 years WHITEPAPER 0% 100% 80% 60% 40% 20% 1 2 3 4 5 Dense Urban Urban Rural 0 2,500,000 2,000,000 1,500,000 1,000,000 500,000 3,000,000 Dense Urban Urban Rural Total 3,500,000 2008 2009 2010 2011 2012 75% 10% 15% 60% 10% 30% 50% 10% 40% 40% 5% 55% 30% 5% 65% AMR12.2 R99 PS HSDPA 2011201020092008Usage 2012
  21. 21. 18 WHITEPAPER Dense Urban Urban Rural 53% 35% 12% WiFi Network Figure 20: WiFi networks in dense urban, urban and rural areas Price of Upgrade Transmission cost in Asia Cost of New Codes, Carriers and Sites Figure 21: Transmission Cost in Asia (in USD) Figure 22: Costs of new codes, carriers and sites Network Assumptions For this TCO study and calculation, the following network assumptions are made: 1. Transmission is rented, hence it falls under OPEX. 2. Site increment is based on 1,000 sites/year to improve coverage and capacity (90% coverage of 300,000km2 area). 3. Subscriber growthis projected at 50% per year. 4. Network is based on UMTS2100. 0 25,000 20,000 15,000 10,000 5,000 30,000 5 codes 1 carrier New Site $0 $1,000 $800 $600 $400 $200 $1,200 $1,400 $1,600 E1 (2Mbps) STM-1 (10Mbps) GE (2Mbps) GE (4Mbps) GE (10Mbps)
  22. 22. 19 5. E1 is used to provide 3.6Mbps; STM-1 for 7.2Mbps, FE for 14.4Mbps and 21.1Mbps. 6. All Node B’s can support 2 IMA groups (16E1) and capacity is ready. 7. All Node B’s comprises 3 sectors. 8. 7.2Mbps is single carrier (1 HSPA+ and 1 R99), 14.4Mbps dual carrier (1 HSPA, 1 for R99) 9. Maximum deployment of 2 carriers. 10. Transmission is calculated based on DL traffic only. 11. 20% transmission buffer is allowed for Capacity Planning. 12. WiFi offload for HSPA + R99 PS only. 13. All Node B’s are upgradable to HSPA 14.4Mbps (15 codes, 64QAM, 2 carrier) but not upgradeable to HSPA+ (which requires Enhanced CELL_FACH, CPC (Continues Packet Connectivity). 14. MBMS and HSUPA are not considered within 5 years roadmap (to simplify calculation of CE). 15. All Node B’s purchased supports HSPA+ Phase I 21.1Mbps (not HSPA+ Phase II 28.8Mbps). 16. HSDPA does not consume CE. WHITEPAPER
  23. 23. 20 Cost (OPEX) Savings IUB (Transmission) Savings In a five-year period and using Greenpacket’s ICMP to facilitate data offload to WiFi, only USD95 million is spent on IUB transmission as opposed to USD105.83 million if no data offloading was carried out. Hence, within five years, USD28.22 million is saved for 7000 Node B’s. Figure 23: IUB transmission TCO over 5 years Figure 24: IUB transmission savings over 5 years Node B Savings For Node B, Greenpacket calculated the price difference for SF Codes, Transmission Power and Channel Element (CE). Figure 25: Price difference for code and power upgrade Figure 26: Price difference for channel element WHITEPAPER Price Difference for CEPrice Difference for Code and Power Upgrade SavingsIUB transmission - 5 years TCO $2 $0 $4 $6 $8 $10 $12 Year 1 Year 2 Year 4 Year 5Year 3 USD (mil) Without WiFi With WiFi Offload USD 95 million USD 105.83 million USD (mil) $90 $95 $105 $110$100 $5 $0 $10 $15 $20 $25 $30 $35 $40 $45 Year 1 Year 2 Year 4 Year 5Year 3 USD (mil) $1 $0 $2 $3 $4 $5 $6 $7 USD (mil) Year 1 Year 2 Year 4 Year 5Year 3 Difference Total Savings (Code, Power & CE) of ~43.78 mil over 5 years for 7000 Node B’s Total Savings of ~28.22mil over 5 years for 7000 Node B’s
  24. 24. 21 Total Savings WHITEPAPER Total Savings Savings of 16% (of OPEX) or 2.9% (of TCO) With a operational expenditure of USD 300 million/year, an operator can save USD 8.7 million/year through data offloading IUB Transmission Savings of 12% (of OPEX) or 2.6% (of TCO) Node B (Codes, Power & CE) Savings of 4% (of OPEX) or 0.3% (of TCO) NETWORK TCO CAPEX (27%) Purchasing (14%) Deployment (13%) Operations (60%) Maintainance (13%) OPEX (73%) Maintenance 11.0% Man Power 3.7% Equipment 5.4% Transmission 1.4% Equipment Accessory 5.4% Antenna 1.4% Site Rental 21.9% Power 7.3% Consumption Leased Line 21.9% Hardware & 7.3% Software Site Acquisition 2.7% Installation 2.7% Civil Works 8.1%
  25. 25. 22 WHITEPAPER Find Out How Much You Can Save Through Data Offloading! Greenpacket welcomes you to embark on the offloading journey today and enjoy tremendous cost savings on your network operations. At Greenpacket, we understand the demands placed on Operators like you. That is why our solutions are designed to give you the capacity to constantly deliver cutting-edge offerings without exhausting your capital and operating expenditures. With Greenpacket, limitless freedom begins now! Free Consultation If you would like a free consultation on how you can start saving network cost through data offloading, feel free to contact us at marketing.gp@greenpacket.com kindly quote the reference code, WP0710DL when you contact us. As part of the consultation, we will be happy to walk-through your network’s TCO and determine how much savings you would gain by offloading data.
  26. 26. 23 References 1. Telecoms: At the starting line – The race to mobile broadband by Gareth Jenkins and Jussi Uskola, Deutsche Bank. 2. Towards a Profitable Mobile Data Business Model by Bridgewater Systems 3. Sharing the Load by Bridgewater Systems 4. Mobile Broadband: Still Growing But Realism Sinks In by Telecom Asia (January/February 2010) 5. Mobile Communications 2008: Green Thinking Beyond TCO Consideration, Kevin Li, In-Stat WHITEPAPER
  27. 27. About Green Packet Greenpacket is the international arm of the Green Packet Berhad group of companies which is listed on the Main Board of the Malaysian Bourse. Founded in San Francisco’s Silicon Valley in 2000 and now headquartered in Kuala Lumpur, Malaysia, Greenpacket has a presence in 9 countries and is continuously expanding to be near its customers and in readiness for new markets. We are a leading developer of Next Generation Mobile Broadband and Networking Solutions for Telecommunications Operators across the globe. Our mission is to provide seamless and unified platforms for the delivery of user-centric multimedia communications services regardless of the nature and availability of backbone infrastructures. At Greenpacket, we pride ourselves on being constantly at the forefront of technology. Our leading carrier-grade solutions and award-winning consumer devices help Telecommunications Operators open new avenues, meet new demands, and enrich the lifestyles of their subscribers, while forging new relationships. We see a future of limitless freedom in wireless communications and continuously commit to meeting the needs of our customers with leading edge solutions. With product development centers in USA, Shanghai, and Taiwan, we are on the cutting edge of new developments in 4G (particularly WiMAX and LTE), as well as in software advancement. Our leadership position in the Telco industry is further enhanced by our strategic alliances with leading industry players. Additionally, our award-winning WiMAX modems have successfully completed interoperability tests with major WiMAX players and are being used by the world’s largest WiMAX Operators. We are also the leading carrier solutions provider in APAC catering to both 4G and 3G networks and aim to be No. 1 globally by the end of 2010. For more information, visit: www.greenpacket.com. Copyright © 2001-2010 Green Packet Berhad. All rights reserved. No part of this publication may be reproduced, transmitted, transcribed, stored in a retrieval system, or translated into any language, in any form by any means, without the written permission of Green Packet Berhad. Green Packet Berhad reserves the right to modify or discontinue any product or piece of literature at anytime without prior notice. San Francisco · Kuala Lumpur · Singapore · Shanghai · Taiwan · Sydney · Bahrain · Bangkok · Hong Kong Associate Member

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