Wifi Offload Whitepaper

7/31/2019 Wifi Offload Whitepaper

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www.greenpacket.com

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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 massivedemands 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 operators 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 muchoperators can save by offloading data to a secondary network such as WiFi. Data offloading directly

impacts 36.5% of a networks 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 Bs.

By Jonathan Ang | July 2010

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Contents

Can Somebody Define Network Congestion? 01

Where Network Congestion Occurs? 04Network 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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01

Can Somebody Define Network Congestion?

Network congestion is at the top of everyones 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 users 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 cellsand 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 networks 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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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.

Lets 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


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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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04

Where Network Congestion Occurs?

To help understand where network congestion occurs, lets 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 transmissionpoints connecting each access point. Todays 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

SCESCP

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 BE1

Node B

E1

Node B

E1

PSTNISDN

SS7

Internet,Intranet

OtherPLMN

GPRSbackbone


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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 inthe next 12 months

Won't be a restraint on mobile servicesfor 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


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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 Bs 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 inthe next 12 months

Won't be a restraint on mobile servicesfor the foreseeable future

Don't know

36%

15%

28%

21%

Do you think radio access network capacity is...


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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? Lets 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


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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


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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 CEs 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 stations 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.


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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 networks total cost or TCO has to

be understood.

A networks 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 networks 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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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

operators 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

2Bridgewater 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 controlNew 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%


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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 Greenpackets Intouch Connection Management Platform (ICMP), an easy-to-use,

single-client connection management solution, innovatively conceptualized from Mobile IP technology.

Figure 10: Greenpackets Intouch Connection ManagementPlatform (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.


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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 Bs cost only.

Our dimensioning tools were used to study an operator in Asia Pacific and these data were obtained:

i. The operators 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 Bs 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%)


OPEX (73%)

Maintenance 11.0%

Man Power 3.7%

Equipment 5.4%


Accessory 5.4%

Antenna 1.4%

Site Rental 21.9%


Leased Line 21.9%



Installation 2.7%

Civil Works 8.1%

0

20

40

60

80

100

~14%

~13%

~60% ~13%

Purchasing Deployment Operation Maintenance TOTAL

Data offloading directlyimpacts 36.5% of TCO


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Source: Greenpacket

Figure 12: Network factors considered by Greenpacket for data offloading calculation

Operators 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.

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Input

Network Scale &

Node B Distribution

Trafc Prole

WiFi Network

HSPA Evolution

Subscriber Prole

Equipment Vendor

Price of Upgrade

Iu-CS

Iu-PS

PS Signaling

PS Trafc

CS Signalling

CS Trafc

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

Trafc Prole WiFi NetworkHSPA Evolution

Subscriber Prole

Equipment Vendor

Price of Upgrade Assumptions


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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


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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


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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)


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5. E1 is used to provide 3.6Mbps; STM-1 for 7.2Mbps, FE for 14.4Mbps and 21.1Mbps.

6. All Node Bs can support 2 IMA groups (16E1) and capacity is ready.

7. All Node Bs 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 Bs 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 Bs purchased supports HSPA+ Phase I 21.1Mbps (not HSPA+ Phase II 28.8Mbps).

16. HSDPA does not consume CE.

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Cost (OPEX) Savings

IUB (Transmission) Savings

In a five-year period and using Greenpackets 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 Bs.

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


USD (mil)

$1

$0

$2

$3

$4

$5

$6

$7

USD (mil)


Difference

Total Savings (Code, Power & CE) of ~43.78 mil over 5 years for 7000 Node Bs

Total Savings of ~28.22mil over 5 years for 7000 Node Bs


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Total Savings

WHITEPAPER

Total SavingsSavings 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%)


OPEX (73%)

Maintenance 11.0%

Man Power 3.7%

Equipment 5.4%


Accessory 5.4%

Antenna 1.4%

Site Rental 21.9%


Leased Line 21.9%



Installation 2.7%

Civil Works 8.1%


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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 l ike a free consultation on how you can start saving network cost through data offloading, feel free to contact

us at [email protected] quote the reference code, WP0710DL when you contact us. As part of the

consultation, we will be happy to walk-through your networks TCO and determine how much savings you would gain by

offloading data.


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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


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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 Franciscos 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 worlds 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.

San Francisco Kual a Lumpur Singapore Shanghai Taiwan Sydney Bahra in Bangkok Hong Kong

Documents

Wifi Offload Whitepaper