Network Capacity Calculation

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Capacity Boosting Solution

White Paper - Network Capacity Calculation

March/2009

TRIANGULUM PTE LTD. CONFIDENTIAL

Copyright 2009 Triangulum PTE LTD., All Rights Reserved

The information contained in this document is the property of Triangulum LTD. Except as specifically authorized in writing

by Triangulum PTE LTD., the holder of this document shall keep the information contained herein confidential and shall

protect same in whole or in part from disclosure and dissemination to third parties and use same for evaluation, operation

and maintenance purposes only.

The content of this document is provided for information purposes only and is subject to modification. It does not

constitute any representation or warranty from Triangulum LTD as to the content or accuracy of the information contained

herein, including but not limited to the suitability and performances of the product or its intended application.

Leveraging Knowledge. Triangulum PTE LTD., the Triangulum logo are trademarks of Triangulum PTE LTD. All othertrademarks are the property of their owners.


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Page 2/19 Triangulum PTE Ltd. confidential

CONTENT

1. INTRODUCTION ........................................................................................................ 3

1.1. OBJECT ......................................................................................................................... 3

1.2. SCOPE OF THIS DOCUMENT............................................................................................... 3

1.3. AUDIENCE FOR THIS DOCUMENT......................................................................................... 3

2. EXECUTIVE SUMMARY ............................................................................................. 4

3. NETWORK CAPACITY ................................................................................................ 5

3.1. CAPACITY AND QUALITY OF SERVICE.................................................................................... 5

3.1.1 Circuit switched QoS ........................................................................................... 63.1.2 Packet switched QoS ........................................................................................... 63.1.3 Network utilization.............................................................................................. 7

3.2. CLASSICAL APPROACH...................................................................................................... 7

3.2.1 Signaling capacity ................................................................................................ 83.2.2 Circuit switch capacity......................................................................................... 93.2.3 Packet switch capacity ...................................................................................... 10

3.3. ADVANCED TRAFFIC MANAGEMENT APPROACH................................................................... 12

3.3.1 Resource utilization ........................................................................................... 123.3.2 Traffic mix management ................................................................................... 133.3.3 New approach for capacity assessment ........................................................... 13

4. CASE STUDY ............................................................................................................ 15

4.1. NETWORK DESCRIPTION................................................................................................. 15

4.2. IMPLEMENTATION RESULTS............................................................................................. 15

5. CONCLUSIONS ......................................................................................................... 19


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1. INTRODUCTION

1.1.

OBJECTThis white paper describes Triangulum's network capacity boosting solution.

1.2.

SCOPE OF THIS DOCUMENT

This white paper contains theoretical background for network capacity

estimation methods, case studies and field results for network capacity

boosting solution implementation.

1.3. AUDIENCE FOR THIS DOCUMENT

Strategic planning technical department.

Network planning and optimization department.


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2. EXECUTIVE SUMMARY

The strong pressure on revenue margins has forced mobile network to re-

examine their approach to mobile network infrastructure investment. Many

advanced operators worldwide are examining different capacity extensions

techniques in order to drive a much lower cost of network ownership.

Designed to cope with the new challenges mobile operator currently facing,

Triangulum has come with a unique capacity extension solution, allowing

mobile operators to benefit from significant operational costs reduction,

while still maintaining a very high Quality of Service for end users and without

investing further money in new infrastructure for capacity extension.

This white paper describes the benefits of implementing Triangulum's

network capacity extension solution and describes case study based on real

implementation on very complicate GSM network.

This white paper is intended for global system for mobile communications(GSM) operators interested in understanding on how capacity extension

solution can help to increase network efficiency and save investments for

GSM radio network expansion.

This white paper contains theoretical background, case studies and field

results for network capacity extension solution implementation.


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3. NETWORK CAPACITY

The network capacity is a keyword for efficient network and operator profit.

But there is a very heavy tradeoff that should be solved in order to achieve

those profit targets from one hand this is a traffic losses and customer

satisfaction due to Quality of Service and from other hand there are

investments for network infrastructure expansion (Capital Expenses CapEx)

and deployment limitations.

Correctly designed network capacity will prevent traffic losses, will provide

high Quality of Service for end users and will minimize investments into

network expansion once network will grow.

There are few different techniques that providing an ability to estimate

network capacity. But prior to network capacity calculation some important

criteria should be defined in order to provide correct results.

3.1.

CAPACITY AND QUALITY OF SERVICE

The Quality of Service definition becomes very complicate for modern GSM

networks due to various traffics' types. A mix of signaling, voice and data

traffics causing headache for network operation departments due to fact that

end user Quality of Service cannot be measured efficiently.

The most common approach to measure Quality of Service in the network is

to define a certain set of Key Performance Indicators (KPIs) and monitor them

over the time. Once some KPIs will exceed predefined thresholds, then a

certain action should be performed in the network in order to bring this KPI

back to normal behavior. This process very often called "Optimization".


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3.1.1 CIRCUIT SWITCHED QOS

Some of well known KPIs are representing a circuit switched network capacity

issues:

SDCCH Congestion Rate

TCH Congestion Rate

It is very important to note that in order to estimate an end user perceived

Quality of Service, mentioned above KPIs should represent only congestion

events that affecting the end user experience and not a condition of "no

available resources" at specific network element. In other words, the end

user related congestion is a ratio between blocked and initiated call attempts

(Call Congestion), while often many infrastructure vendors are suggesting

usage of "Time Congestion" KPIs that are representing only network

resources conditions and not an end user experience.

Mentioned above KPIs may point out on specific bottle neck in the circuit

switched network and initiate a troubleshooting activity. In case of capacity

related KPIs a typical troubleshooting activity is a network resource

expansion i.e. new equipment installation.

3.1.2 PACKET SWITCHED QOS

The Quality of Service definition for packet switched data services is much

more complicated. This is due to reason that different data services have

different requirements for Quality of Service. The end user perceived

experience for data services can be represented by two measures:

Service start - delay

Service end - data throughput

The following graph shows dependency of different data services from two

major KPIs that affecting end user experience:


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3.1.3 NETWORK UTILIZATION

In order to provide a short cycle for Return of Investments (ROI), each

operator should closely evaluate the efficiency of each network element. It is

very important that already deployed infrastructure will be fully utilized for

revenue generating services.

For cell efficiency evaluation the cell utilization KPIs should be monitored.

The definition of utilization KPI is pretty simple and described as ratio

between amount of served traffic and available cells' resources (timeslots).

3.2. CLASSICAL APPROACH

The most commonly used technique is an Erlang B, which calculates cell

capacity under given Grade of Service value.

The Erlang B calculation allows to get a possible amount of traffic that can be

served by specific cell without exceeding a predefined blocking level. The

Erlang B calculation is assuming a certain model of voice traffic behavior,

which is applicable for on-going network dimensioning process. The

dimensioning calculation performed periodically per cell basis and as results

new requirements for hardware (TRXs) installation released for


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implementation. Sometimes, due to hardware or spectrum limitations

(inability to extend an existing cell), new requirements for new sites

deployment released upon dimensioning calculation cycle is finished.

Typically operators are defining a set of threshold for every cell configuration

in order to simplify an operational procedures and decision making for new

hardware deployment.

But implemented radio network features, like: dynamic half rate, AMR half

rate, load sharing and some more, may seriously affect a standard Erlang B

calculation's results due to more complex traffic behavior. This fact may lead

to over- or underestimation of network capacity.

An underestimation of network capacity may lead to significant Quality of

Service degradation for end users and also this will lead to real traffic losses

i.e. revenue losses for operator.

An overestimated (to be on the safe side) network capacity may lead to

excessive network infrastructure deployment (CapEx and OpEx), which

reduces at the end a profit margin for operator.

3.2.1 SIGNALING CAPACITY

The signaling processes in GSM network are responsible for the following

activities:

Mobility Management

SMS sending

Call Setups

It is possible to differentiate signaling traffic according to revenues, where

SMS and Call Setups are revenue generators, while Mobility Management

signaling traffic is consuming network resources without any revenue

generation.


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It is very important that Mobility Management signaling traffic will be as

lower as possible and will not occupy network resources that can be used for

revenue generating traffic types. Excessive Mobility Management signaling

traffic will lead to increase of network resources utilization and may affect

Quality of Service for end users or even lead to higher requirements for new

hardware.

3.2.2 CIRCUIT SWITCH CAPACITY

Circuit switched cell capacity typically estimated by Erlang B calculation,

where each combination of timeslot configuration and given Grade of Service

provides the amount of offered traffic:

Grade of Service

Lines 0.01 0.02

14 7.35 8.20

22 13.65 14.85

30 20.30 21.90

37 26.35 28.25

45 33.40 35.60

52 39.70 42.10

There is a relation between Offered traffic and Served traffic:

Served Traffic = Offered Traffic/(1+GoS)

Since Erlang B is just a mathematical model, it does not represent an instant

traffic load and end user behavior.


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The following graph shows an instant traffic behavior on specific cell, average

traffic volume (erlangs) and available number of timeslots for traffic serving:

Available performance counters are only allowing measurements for Average

Busy Timeslots KPI, which is equal to Served traffic.

3.2.3 PACKET SWITCH CAPACITY

By definition the packet switching services in GSM network performing

according Best Effort approach, where circuit switched traffic has priority

over packet switched. Due to instant voice traffic behavior there is always can

be a situation where circuit switched traffic occupies all available cell's

resources and packet switched traffic cannot be served.

The following graph shows typical instant traffic behavior and resources

availability for packet switched (PS) traffic:

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Erlangs

Time

Offered Traffic Average Busy TS Max Available TS

Congestion


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In order to overcome packet switched resource availability, often operators

are implementing dedicated packet switched resources for each cell. This

approach allows to provide certain Quality of Service for data traffic, while a

significant network expansion required. The following table contains a real

network TRX expansion sensitivity analysis for introduction network-wide

dedicated packet switch resources:

On-going Voice

Expansion

1 Dedicated

PS TS

3 Dedicated

PS TSs

4 Dedicated

PS TSs

New TRXs required 301 668 1442 2156

Total cells 5493 5493 5493 5493

Cells to be expanded,(%) 5.48% 12.16% 26.25% 39.25%

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No PS resources


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3.3. ADVANCED TRAFFIC MANAGEMENT APPROACH

Our Capacity Extension Solution is based on advanced software network

optimization, which utilizes existing radio network features without changing

existing hardware configurations. The advanced traffic management

optimization allows to change a classical network expansion procedures,

while significantly reducing requirements for hardware expansion together

with significant improvement of end user perceived experience.

3.3.1 RESOURCE UTILIZATION

The most important effect from implementation of advanced traffic

management approach is a change of instant traffic behavior. The following

graph shows effect of advance traffic management implementation on cell

instant traffic behavior:

As it clearly shown above, the cell utilization become much higher than in

classical approach, while congestion management technique allows to

improve an end user perceived experience.

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3.3.2 TRAFFIC MIX MANAGEMENT

New traffic management approach allows an efficient circuit and packet

traffic mix with a significant Quality of Service improvement, while no new

requirements for infrastructure deployment. The new technique gives an

opportunity to dedicate a sufficient resource for packet switched traffic

together with ability to share additional resources with circuit switched traffic

on very efficient way, while overall capacity become even higher than it was

prior to implementation. The following graph shows ability of such traffic

management technique:

3.3.3 NEW APPROACH FOR CAPACITY ASSESSMENT

The main question that should be asked after reading this document is how

to calculate a real network capacity and how much this affects end users?

In order to answer those questions we are suggesting to estimate a network

capacity not according to classical way, where the overall network capacity is

accumulated from each cell capacity estimation (Erlang B), rather as amount

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of additional traffic that can be served by network without affecting user

experience.

The proposed method provides a clear understanding of available network

resources and shows the way for significant Capital Expenditures (CapEx)

saving.


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4. CASE STUDY

Our advanced traffic management strategy was implemented in few

complicated mobile networks. Each implementation shows dramatic increase

of network capacity. Typical capacity extension measured values are between

15% and 30%, while in some cases it was even 40%. A network capacity

increase without new hardware is leading to significant reduction in new

infrastructure requirements together with significant reduction of daily

network operation costs due to better network performance and better user

experience.

This case study is based on recent project performed by Triangulum on one of

the most busiest and complicated network around the world. The overall

project timeframe was 4 weeks only.

4.1.

NETWORK DESCRIPTION

The performance improvement solution had been implemented on network

with following specification:

Environment Dense Urban

1800 Cells 250

900 Cells 300

Available 1800 band 80 ARFCNs

Available 900 band 72 ARFCNs

Average TRXs per Cell 8

4.2.

IMPLEMENTATION RESULTS

The following table shows the network performance benchmark prior to

capacity boosting solution implementation (before) and after:


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KPI Before After Delta,%

SDCCH Assign Fail 0.93 0.40 56.99

SDCCH Drop 0.87 0.76 12.64

TCH Assign Fail 0.74 0.65 12.16

TCH Drop 0.73 0.63 13.70

SDCCH Traffic 930.87 847.81 8.92

TCH Traffic 4044.54 4769.85 17.93

SDCCH Cong 0.52 0.34 34.62

Call Congestion 2.82 0.07 97.52

TCH HR Traffic % 8.30 4.83 41.87

Bad Quality UL 2.40 2.00 16.65

Bad Quality DL 2.43 1.87 23.07

1800 TCH Traffic % 33.37 66.04 97.9

900 TCH Traffic % 66.65 33.96 -49.05

GPRS Traffic 2069.93 2845.15 37.45

EDGE Traffic 878.89 1222.35 39.08

TBF CONGESTION 0.32 0.03 90.63

The following graph shows the major project implementation stages and

results in network capacity extension, where dependency of traffic growth

(more than 20%) and Call Congestion disappears and network is able to

accumulate more traffic without losses:

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TCH Traffic Call Congestion


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The same behavior pattern is recognizable for packet switched traffic, where

overall traffic growth was more than 35% for GPRS and EDGE traffic. The

following graph shows dependency of GPRS/EDGE traffic volume versus TBF

congestion:

The half rate vocoder utilization is significantly reduced, while traffic volume

growth was more than 20%:

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EDG E_ TRA FF IC_ ERLA NG G PRS_ TRAF FIC_ ERLA NG TBF_ CONG ES TION


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The implemented traffic management strategy helps to redistribute traffic in

the network between 900 and 1800 bands. This allows to raise utilization of

existing 1800 band cells, while 900 band cells are ready to accumulate traffic

growth without requirements for additional hardware:

The overall calculated capacity is presented in the following table:

Voice traffic growth 20%

Half rate utilization decrease 4%

Voice traffic shifted to 1800 Band 31%

Original capacity reserve 10%

Total Capacity Extension 45%

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1800 Traffic (%) 900 Traffic (%)


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5. CONCLUSIONS

The advanced traffic management approach, proposed by Triangulum, can

help mobile network to achieve a double benefit, first by helping to extend

the current network capacity without investing in new infrastructure and

decrease operational costs while maintaining the quality targets, and second

by implementing new techniques and methods that will help the mobile

operator to maintain and operate his network with less efforts and with

higher quality level.

For further discussion on how advanced traffic management approach could

extend a network capacity, please, contact Triangulum PTE Ltd.

Documents

Network Capacity Calculation