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.

1. www.greenpacket.com
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Operators Can Save $14 Million Yearly
Through Data Offloading
A TCO Study & Calculation on Data Offloading

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.
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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
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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.
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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.
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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. 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
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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
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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. 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.
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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. 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
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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. 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
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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. 08
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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. 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.
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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. 10
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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. 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.
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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
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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. 13
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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. 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
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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. 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. 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
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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. 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
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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. 18
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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. 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.
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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
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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. 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. 22
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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. 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
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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.
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