umts capacity planning

HUAWEI TECHNOLOGIES CO., LTD. Huawei Confidential

Internal

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www.huawei.com

UMTS Capacity Planning

ISSUE 4.0

RNP Staff Training Dept.


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Page 3

Foreword WCDMA is a self-interference system

WCDMA system capacity is closely related to

coverage

WCDMA network capacity has the “soft capacity”

feature

The capacity planning of the WCDMA network is

performed under certain traffic models


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Page 4

Objectives

After this course, you will：

Understand the factors that restrict the

WCDMA network capacity

Understand the methods and procedures

of estimating multi-service capacity

Understand the key technologies for

enhancing network capacity


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Page 5

1 Traffic Model

2 Uplink capacity analysis

3 Downlink capacity analysis

4 Multi-service capacity estimation

5 Network estimation procedure

6 Capacity enhancement technologies

Contents


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Page 6

1 Traffic Model

1.1 Overview of traffic model

1.2 CS traffic model

1.3 PS traffic model

Contents


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Page 7

Service Overview

The WCDMA system supports multiple services

Variable-rate services (e.g. AMR voice)

Combined services (e.g. CS & PS)

High-speed data packet services (384k service)

Asymmetrical services (e.g. stream service )

Large-capacity and flexible service bearing


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Page 8

QoS Type

Service characters examples

Real-tim

e

cate

go

ry

Conversationa

l

It is necessary to maintain the time relationship

between the information entities in the stream.

Small time delay tolerance, requiring data rate

symmetry

Voice service,

videophone

Streaming Typically unidirectional services, high requirements

on error tolerance, high requirements on data rate

Streaming

multimedia

No

n re

al-tim

e

cate

go

ry

Interactive

Request-response mode, data integrity must be

maintained. High requirements on error tolerance,

low requirements on time delay tolerance

Web page browse,

network game

Background Data integrity should be maintained. Small delay

restriction, requiring correct transmission

Background

download of

Email


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Page 9

Objectives of Setting Up Traffic Model

In order to determine the system configuration, we need to

determine the capacity of the air interface first

In the data service, different transmission model will generate

different system capacities

We need to set up an expected data transmission model of the

customer so that we can plan the network properly

In order to set up a right model, the operator should provide

some statistic data as reference


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Page 10

Traffic Model

Traffic model is a means of researching the capacity features

of each service type and the QoS expected by the users who

are using the service from perspective of data transmission

In the data application, the user behaviour research mainly

forecasts the service types available from the 3G, the number

of users of each service type, frequency of using the service,

and the distribution of users in different regions


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Page 11

The Contents of Traffic Model

Network configuration

User behaviour

Service pattern

Traffic Model

Results

Capacity planning

Coverage planning


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Page 12

Typical Service Features Description

Typical service features include the following feature

parameters:

User type (indoor ,outdoor, vehicle)

User’s average moving speed

Service Type

Uplink and downlink service rates

Spreading factor

Time delay requirements of the service

QoS requirements of the service


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Page 13

1 Traffic Model




Contents


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Page 14

CS Traffic Model

Voice service is a typical CS services.

Key parameters of the model

Penetration rate

BHCA Mean busy-hour call attempts

Mean call duration (s)

Activation factor

Mean rate of service (kbps)


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Page 15

CS Traffic Model Parameters

Mean busy-hour traffic (Erlang) per user = BHCA * mean call

duration /3600

Mean busy hour throughput per user (kbit) (G) = BHCA * mean

call duration * activation factor * mean rate

Mean busy hour throughput per user (bps) (H) = mean busy hour

throughput per user * 1000/3600


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Page 16

1 Traffic Model




Contents


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Page 17

PS Traffic Model

The most frequently used model is the packet service

session process model described in ETSI UMTS30.03


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Page 18

PS Traffic Model

Active Dormant

Data Burst Data Burst Data Burst

Packet Call

Session

Packet Call Packet Call

Downloading Downloading

Active Dormant Dormant Active


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Page 19

PS Traffic Model Parameters

业务模型

Packet Call Num/Session

Packet Num/Packet Call

Packet Size (bytes)

Reading Time (sec)

Typical Bear Rate (kbps)

BLER


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Page 20

Parameter Determining

The basic parameters in the traffic model are determined

in the following ways:

Obtain numerous basic parameter sample data from the existing

network

Obtain the probability distribution of the parameters through

processing of the sample data

Take the distribution most proximate to the standard probability

as the corresponding parameter distribution through comparison

with the standard distribution function


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Page 21

NBLER

BLERNBLERNBLERNBLERNN n *1

1**** 32


Typical Bearer Rate (kbps)：

Bearer rate is variable in the actual transmission process

BLER

In the PS service, when calculating the data transmission time, the

retransmission caused by erroneous blocks should be considered.

Suppose the data volume of service source is N, the air interface

block error rate is BLER, the total required data volume to be

transmitted via the air interface is:


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Page 22

PS User Behaviour Parameters

User behavior

Penetrating Rate

BHSA

User Distribution (High,

Medium, Low end)


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Page 23

PS User Behaviour Parameters

Penetration Rate

The percentage of the users that activates this service to all

the users registered in the network.

BHSA

The times of single-user busy hour sessions of this service

User Distribution (High, Medium, Low end)

The users are divided into high-end, mid-end and low-end

users. Different operators and different application

situations will have different user distributions


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Page 24


Session traffic volume（Byte）： Average traffic of single

session of the service

Data transmission time (s) ： The time in a single session

of service for purpose of transmitting data.

Holding Time（s）： Average duration of a single session

of service

eTypicalRat

fficVolumeSessionTra

BLERsissionTimeDataTransm

1000/8**

1

1)(

)(

Re*)1/(

sissionTimeDataTransm

adingTimeSessionlNumPackketCaleHoldingTim

)/(*)/(*)( SessionNumPacketCallPacketCallPacketNumPacketSizefficVolumeSessionTra


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Page 25

Active factor:

The weight of the time of service full-rate transmission among

the duration of a single session.

Busy hour throughput per user (Kb):

PS throughput equivalent Erlang formula (Erlang)

eHoldingTim

issionTimeDataTransmorActiveFact

1000/8**/ fficVolumeSessionTraBHSAuserroughputBusyHourTh


)3600

(_

orActiveFactredRateTypicalBea

nEviromentApplicatioderTypicalroughputUnBusyHourThgRatePenetratinUserOfDiffrentPercentageErlangData


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Page 26

1 Traffic Model






Contents


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Page 27

Basic Principles

In the WCDMA system, all the cells share the same

frequency, which is beneficial to improve the system

capacity. However, co-frequency multiplexing causes

interference between users. This multi-access

interference restricts the capacity

The radio system capacity is decided by uplink and

downlink. When planning the capacity, we must analyze

from both uplink and downlink perspectives


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Page 28

NotherownTOT PIII

Uplink Interference Composition

：Interference from the users of

this cell

：Interference from users of

adjacent cell

：Noise floor of the receiver

ownI

otherI

NP


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Page 29


Receiver noise floor PN

– K：Boltzmann constant, 1.38×

– T：Kelvin temperature, normal temperature: 290 K

– W：Signal bandwidth, WCDMA signal bandwidth

3.84MHz

– 10lg(KTW) = -108dBm/3.84MHz

NF = 3dB (typical value of macro cell BTS)

NFWTKPN )**log(10

KJ /10 23

MHzdBmNFWTKPN 84.3/105)**log(10


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Page 30


：Interference from users of this cell

Interference that every user must overcome:

is the receiving power of the user j , is active factor

Under the ideal power control :

Hence, :

The interference from users of this cell is the sum of power of all the

users arriving at the receiver

jjjTOT

j

jvR

W

PI

PNoEb

1/

N

jown PI1

jjj

TOTj

vR

W

NoEb

IP

1

/

11

jtotal PI

jVjP

jP

ownI


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Page 31


: Interference from users of adjacent cell

The interference from users of adjacent cell is difficult to

analyze theoretically, because it is related to user

distribution, cell layout, and antenna direction diagram.

Adjacent cell interference factor ：

When the users are distributed evenly

- For omni cell, the typical value of adjacent cell

interference factor is 0.55

- For the 3-sector directional cell, the typical value of

adjacent cell interference factor is 0.65

own

other

I

Ii

otherI


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Page 32

Define

Then

Uplink Interference Analysis

N

N

jjj

TOT

NotherownTOT

P

vR

W

NoEb

Ii

PIII

1

1

/

11

1

jjj

j

vR

W

NoEb

L1

/

11

1

N

N

jTOTTOT PLiII 1

1


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Page 33

Obtain

N

j

NTOT

Li

PI

1

11

1

Uplink Interference Analysis

Suppose that:

All the users are 12.2 kbps voice

users, the demodulation threshold

Eb/No = 5dB

Voice activation factor vj = 0.67

Adjacent cell

– interference factor

– i = 0.55


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Page 34

Uplink Load Factor

Define the uplink load factor

When the load factor is 1, is infinite, and the

corresponding capacity is called “threshold capacity”.

Under the above assumption, the threshold capacity is

approx 96 users.

N

jjj

N

jUL

vR

W

EbvsNo

iLi11

111

111

TOTI


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Page 35

Load Factor and Interference

According to the above mentioned relationship,

the noise will rise:

1

1 1

11 1

TOT

N

N ULj

INoiseRise

Pi L

50% Load — 3dB

60% Load — 4dB

75% Load — 6dB


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Page 36

Limitation of the Current Method

The above mentioned theoretic analysis uses the

following simplifying explicitly or implicitly:

No consideration of the influence of soft handover

– The users in the soft handover state generates the interference

which is slightly less than that generated by ordinary users.

No consideration of the influence of AMRC and hybrid service

– AMRC reduces the voice service rate of some users, and makes

them generate less interference, and make the system support

more users. (But call quality of such users will be deteriorated)

– Different services have different data rates and demodulation

thresholds. So, we should use the previous methods for analysis,

but it will complicate the calculation process.

– Since the time-variable feature of the mobile transmission

environment, the demodulation threshold even for the same service

is time-variable.


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Page 37

Limitation of the Current Method

Ideal power control assumption

– The power control commands of the actual system have

certain error codes so that the power control process is not

ideal, and reduces the system capacity

Assume that the users are distributed evenly, and the

adjacent cell interference is constant

Considering the above factors, the system simulation is a

more accurate method:

– Static simulation: Monte_Carlo method

– Dynamic simulation


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Page 38

1 Traffic Model






Contents


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Page 39

NotherownTOT PIII

Downlink Interference Composition

: Interference from the users of this cell

: Interference from the users of adjacent cell

: Noise floor of the receiver

ownI

otherI

NP


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Page 40


Receiver noise floor PN

– K Boltzmann constant, = 1.38*

– T Kelvin temperature, normal temperature 290 K

– W Signal bandwidth, WCDMA signal bandwidth 3.84MHz

– NF: Receiver noise figure

10lg(KTW) = -108dBm/3.84MHz

NF = 7dB （ UE typical value ）

NFWTKPN )**log(10

KJ /10 23

MHzdBmNFWTKPN 84.3/101)**log(10


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Page 41


:Interference from downlink DCH of this cell

The downlink users are identified with the mutually orthogonal

OVSF codes. In the static propagation conditions without multi-

path, no mutual interference exists.

In case of multi-path propagation, certain energy will be detected

by the RAKE receiver, and become interference signals. We

define the orthogonal factor α to describe this phenomenon.

– In the formula, PT is a total transmitting power of BTS, which includes

the dedicated channel transmitting power and the common channel

transmitting power

1 Town jj

j

PI

PL

N

jCCHT PPP1

ownI

PT

PLj

UE1

UE2

UEj

…


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Page 42


: Interference from the downlink DCH of adjacent cell

The transmitting signal of the adjacent cell BTS will cause

interference to the users in the current cell. Since the scrambling

codes of users are different, such interference is non-orthogonal

Assume the service is distributed evenly, the transmitting power of

all BTSs will be equal. k,j In the system, there are K adjacent cell

BTSs, where path loss from the number k BTS to the user j is PLk,j.

Hence we obtain:

K

jk

TjotherPL

PI1 ,

1

otherI

UE1

UE2

UEj

Cell 1

Cell 2

Cell k

PL1,j

PLk,j

PT

PT

PT

PL2,j

… …


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Page 43


N

K

jk

T

j

Tj

NotherownTOT

PPL

PPL

P

PIII

1 ,

11

Suppose the power control is desired, we obtain

jjjTOT

j

j

jvR

W

I

PL

P

EbvsNo1

Then

jjTOTj

j

jj PLIvW

REbvsNoP


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Page 44

Because N

jCCHT PPP1

Then

jN

K

jk

j

TTj

N

j

j

jCCH

N

K

jk

T

j

Tj

N

jj

j

jCCH

N

jjTOTj

j

jCCHT

PLPPL

PLPPv

W

REbvsNoP

PPL

PPL

PPLv

W

REbvsNoP

PLIvW

REbvsNoPP

1 ,1

1 ,1

1

1

11



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Page 45

Resolve PT to obtain

N

j

j

jjj

N

jj

j

jNCCH

T

vW

REbvsNoi

PLvW

REbvsNoPP

P

1

1

11

where ij is the adjacent cell interference factor of the user,

defined as:

K

jk

j

jPL

PLi

1 ,



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Page 46

Downlink Interference Analysis

According to the above analysis, we can define the downlink load

factor:

When the downlink load factor is 100%, the transmitting power of the

BTS is infinite, and the corresponding capacity is called “threshold

capacity”.

As different from the theoretic calculation of uplink capacity, and

in the downlink capacity formula are variable related to user position.

Namely, the downlink capacity is related to the spatial distribution of

the users, and can only be determined through system simulation.

N

j

j

jjjDL vW

REbvsNoi

1

1

ja ji


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Page 47

Simulation Result


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Page 48

Simulation Result Analysis

When the transmitting power of the BTS is 43dBm (20W),

the supported maximum number of users is approx 114.

In order to ensure system stability, we do not allow the

mean transmitting power of the BTS to be more than 80%

of the maximum transmitting power, namely, 42dBm. This

way, the supported number of users is 111.


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Page 49

1 Traffic Model






Contents


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Page 50


4.1 Network capacity restriction factors

4.2 Typical capacity design methods

Contents


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Page 51

Capacity Restriction Factors

The WCDMA network capacity restriction factors in the radio

network part include the following:

Uplink interference

Downlink power

Downlink channel code resources (OVSF)

Channel element (CE)

Iub interface transmission resources


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Page 52

Downlink Transmit Power

The downlink transmit power has two parts:

one part is used for common channel, and the

other part for dedicated (traffic) channel.

The transmit power is allocated by the cell to

each user varies with service demodulation

threshold, propagation path loss and the

interference received by the user

The downlink transmit power of the cell is

shared by all the users in the cell

We generally use the simulation method to

analyze the downlink interference.

N

jCCHT PPP1


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Page 53

Downlink Channel Code Resources

The WCDMA network use the codes whose SF is 4~512. The smaller

the SF is, the higher the supported data rate will be.

In the code tree, the allocable codes should meet the following

conditions:

No codes on the path from this code to the root node of code tree are

allocated

No codes in the sub-tree whose root node is this code are allocated

Try to reserve the code words whose SF is small, so as to improve the

utilization efficiency

1

1 -1

1 1

1 1 1 1

1 1 -1 -1

1 -1 1 -1

1 -1 -1 1

C1,0

C2,0

C2,1

C4,0

C4,1

C4,2

C4,3

SF = 1 SF = 2 SF = 4

1

1 -1

1 1

1 1 1 1

1 1 -1 -1

1 -1 1 -1

1 -1 -1 1

C1,0

C2,0

C2,1

C4,0

C4,1

C4,2

C4,3

SF = 1 SF = 2 SF = 4


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Page 54

Downlink Channel Code Resources

Following is an example of code resources allocation

SF 4 8 16 32 64 128 256 512

　　　　　　　　　　　　　　　　　　　　　　　　　　　　┏━●C(256,0):PCPICH 2

　　　　　　　　　　　　　　　　　　　　　　　　┏ 0 ┫

　　　　　　　　　　　　　　　　　　　　　　　　┃　　　┗━●C(256,1):PCCPCH 3

　　　　　　　　　　　　　　　　　　　　┏ 0 ┫

　　　　　　　　　　　　　　　　　　　　┃　　　┃　　　┏━●C(256,2): AICH 6

　　　　　　　　　　　　　　　　　　　　┃　　　┗ 1 ┫

　　　　　　　　　　　　　　　　　　　　┃　　　　　　　┗━●C(256,3): PICH 10

　　　　　　　　　　　　　　　　┏ 0 ┫

　　　　　　　　　　　　　　　　┃　　　┗━●C(64,1):SCCPCH 8

　　　　　　　　　　　　┏ 0 ┫

　　　　　　　　　　　　┃　　　┃　　　┏━●C(64,2):SCCPCH 9

　　　　　　　　　　　　┃　　　┗ 1 ┫

　　　　　　　　　　　　┃　　　　　　　┗━○3

　　　　　　　　┏ 0 ┫

　　　　　　　　┃　　　┗━○1

　　　　┏ 0 ┫

　　　　┃　　　┗━○1

┃

　　　┗━○1

　　　┏━○2

┃

　　　　┗━○3


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Page 55

Channel Element (CE)

The Channel element the quantitative data that measures the

resources logically occupied for service processing

The resource occupied by the service processing is mainly related

to the spreading factor of this service. The smaller the SF is, the

greater the data traffic will be, and more resources will be

occupied

The SF of typical services are:

AMR12.2kbps SF=128

CS64kbps SF=32

PS64kbps SF=32

PS144kbps SF=16

PS384kbps SF=8


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Page 56

Channel element (CE)

If we define the resources required for processing AMR 12.2kbps

services as a channel processing unit, the number of channel

processing units occupied by other services is:

Uplink Downlink

AMR12.2kbps 1 1

CS64kbps 3 2

PS64kbps 3 2

PS128kbps 5 4

PS144kbps 5 4

PS384kbps 10 8


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Page 57

Iub Interface Capacity

The contents transmitted on the Iub interface include:

The user data encapsulated in the AAL2 format (common

channel and dedicated channel)

Signalling data encapsulated in the AAL5 format

BTS operation & maintenance data


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Page 58

Iub Interface Capacity

Factors to be considered when estimating the interface capacity:

Frame coding efficiency. Through segmentation and encapsulation

of the application data at each layer, the data quantity at the bottom

layer will be increased to different extents compared with the

application data at the upper layers

Traffic. More users will generate more data traffic

Maintenance efficiency. Certain bandwidth is required in the

background maintenance for BTS data transmission


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Page 59


4.1 Network capacity restriction factors

4.2 Typical capacity design methods

Contents


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Page 60

Erlang-B Formula (I)

Erlang-B formula is used for estimating the peak traffic that meets

certain call loss rate when the average traffic (Erlang) is given

Erlang-B formula is only used for

Circuit switched services

Single service

The WCDMA system provides CS and PS domain multi-services


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Page 61

Erlang-B Formula (II)

The prerequisite of the Erlang-B is the requests of resources

take on a Poisson distribution, namely, its variance is equal to

its mean value

If, when a service establishes a link, the service requires the

resources which are more than the unit resources, the resource

request is no longer equal to its mean value, and the Erlang-B

formula is not applicable in this case

Comparison of multi-service capacity estimation methods :

Post Erlang-B

Equivalent Erlangs

Campbell’s Theorem


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Page 62

Post Erlang-B（一）

By summing up the capacities required for different services, we

obtain the capacities required for the combined services

No consideration of the resource efficiency of different services

capacity

X Erl data

Y Erl voice


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Page 63

Post Erlang-B (II)

Consider that two services share resources

Service 1: 1 unit resource/connection.12 Erlang

Service 2: 3 unit resources/connection.6 Erlang

Calculate capacity required for each service

Service 1: 12 Erlangs require 19 connections (19 unit

resources), meeting the 2% blocking rate

Service 2: 6 Erlangs require 12 connections (equivalent to

the 36 unit resources of service 1), meeting the 2% blocking

rate

Total 55 unit resources


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Page 64

Post Erlang-B overestimates

the capacity requirements!

Post Erlang-B (III)

Consider that two services use the same resources



Calculate capacity required for each service

Service 1: 12 Erlangs require 19 connections, meeting the 2%

blocking rate

Service 2: 6 Erlangs require 12 connections, meeting the 2%

blocking rate

Total 31 unit resources

However, the reasonable results should be: 18 Erlangs require

26 connections for meeting the 2% blocking rate 2


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Page 65

Equivalent Erlangs (I)

By converting the bandwidth from one service to

another service, combine different services and then

calculate the required capacity

Selecting different services as the measurement

benchmark will lead to different capacity requirements


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Page 66

Equivalent Erlangs (II)

capacity

X Erl data

Y Erl voice

Voice service to data service


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Page 67

Equivalent Erlangs (III)

data service to voice service

capacity

X Erl data

Y Erl voice


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Page 68

Equivalent Erlangs (II)




If using service 1 as measurement benchmark, the two services

are equivalent to 30 Erlangs in total

30 Erlangs require 39 connections (39 unit resources), meeting the 2%

blocking rate

If using service 2 as measurement benchmark, the two services

are equivalent to 10 Erlangs in total

10 Erlangs require 17 connections (equivalent to 51 unit resources of service

1), meeting the 2% blocking rate

The predication results

are not unique!


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Page 69

Campbell’s Theorem (I)

X Erl data

Y Erl voice

Z Erl dummy service traffic

Basic service

capacity

Dummy service channel


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Page 70

The Campbell theorem sets up a combined distribution

Here:

is service amplitude, namely, the channel resources

required for a single link of the service.

is the mean value, v is the variance.

ia

Campbell’s Theorem (II)

cfficOfferedTra

c

aCCapacity ii )(

i

i

i

i

aErlangs

aErlangsv

c

2


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Page 71

Campbell’s Theorem (III)




The system mean value is

The system variance is

The capacity factor c is 1

3063121 iaErlangs

2.23066

vc

6636112 222 iaErlangsv


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Page 72

Campbell’s Theorem (III)

Combined traffic is:

The number of connections for meeting the blocking rate of 2% is 21

For the target services that meet the same GoS, the capacity

required is (calculated on the basis of the unit resource of service 1)

Goal is service 1: C1 = (2.2×21) +1 =47

Goal is service 2: C2 = (2.2×21) +3 =49

For different services, the same GoS requires different capacities.

For the given capacity, the GoS of different services will differ slightly.

63.132.2

30

cfficOfferedTra


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Page 73

The comparison of the different capacity method

Post Erlang-B

Service 1 (1 unit resource/connection, 12Erl) and service 2 (3

unit resources / connection, 6Erl), requiring 55 unit resources in

total

Equivalent Erlangs

Calculated according to benchmark of service 1 (1 unit

resource/connection, 12Erl), a total of 39 unit resources are

required

Calculated according to benchmark of service 2 (3 unit

resources/connection, 6Erl), a total of 51 unit resources are

required

Campbell’s Theorem

In the same conditions, 47~49 unit resources are required in total.


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Page 74

Summary

This chapter deals with the three

methods of estimating the multi-service

capacity

The detailed process of using the

Campbell theorem to calculate the

capacity is described


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Page 75

1 Traffic Model






Contents


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Page 76

Network Estimation Procedure

Cell

radius

Assumption of cell

load and carrier

number

Uplink capacity

dimension , downlink

capacity dimension

User density

Number of

user per cell

Service message

Compare over

Adjustment of cell

load and carrier

number

Yes

No

Cell

area

Balance between capacity and coverage dimension?


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Page 77

1 Traffic Model






Contents


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Page 78

Transmission Diversity －TxDiv

Transmission diversity can enhance the downlink

capacity and coverage

Conclusion of capacity enhancement of transmission

diversity

STTD mode: Capacity increase of 17 ~ 24%

TxAA(1) mode: Capacity increase of 16 ~ 23%

TxAA(2) mode: Capacity increase of 31 ~ 37%


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Page 79

Sectorization

In the dense urban areas and the normal urban areas with

high traffic, increasing sectors of the BTS is a method of

improving the capacity

6-sectors BTS generally use the antenna whose horizontal

lobe is 33º

The capacity of a 6-sector BTS is 1.67 times that of a 3-

sector BTS

The capacity of a 3-sector BTS is 2.77 times that of a omni-

BTS

Thank you www.huawei.com

umts capacity planning

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