05 OWJ100102 WCDMA Radio Network Capacity Planning (With Comment) ISSUE 1.0

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OWJ100102 WCDMA Radio OWJ100102 WCDMA Radio Network Capacity PlanningNetwork Capacity Planning

ISSUE 1.0ISSUE 1.0

HUAWEI TECHNOLOGIES CO., LTD. All rights reserved

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


Upon completion of this course, you will be able to:

Grasp the parameters of 3G traffic model

Understand the factors that restrict the WCDMA network capacity

Understand the methods and procedures of estimating multi-service capacity


Chapter 1 Traffic Model Chapter 1 Traffic Model

Chapter 2 Uplink capacity analysisChapter 2 Uplink capacity analysis

Chapter 3 Downlink capacity analysis Chapter 3 Downlink capacity analysis

Chapter 4 Multi-service capacity estimation Chapter 4 Multi-service capacity estimation

Chapter 5 Network estimation procedureChapter 5 Network estimation procedure



1.1 Overview of traffic model1.1 Overview of traffic model

1.2 CS traffic model 1.2 CS traffic model

1.3 PS traffic model1.3 PS traffic model


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


QoS Type

Real-tim

e category

Conversational

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

Non real-tim

e

category

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.

HUAWEI TECHNOLOGIES CO., LTD. Page 9All rights reserved

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.


Traffic Model

Service pattern 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.


System Configuration

User Behaviour

Service Pattern

Traffic Model Results

The Contents of Traffic Model


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



1.1 Overview of traffic model 1.1 Overview of traffic model




CS Traffic Model

Voice service is a typical CS services. Voice data arrival conforms to the Poisson distribution. Its time interval conforms to the exponent distribution.

Key parameters of the model:

Penetration rate

BHCA - Mean busy-hour call attempts

Mean call duration (s)

Activation factor

Mean rate of service (kbps)


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



1.1 Overview of traffic model 1.1 Overview of traffic model




PS Traffic Model The most frequently used model is the packet service session

process model described in ETSI UMTS30.03.


PS Service Pattern

Data Burst Data Burst Data Burst

Packet Call

Session

Packet Call Packet Call

Downloading Downloading

Active Dormant Dormant Active


Traffic model

PS Service Pattern Parameters

Packet Call Num /Session

Packet Num /Packet Call

Packet Size (bytes)

Reading Time (sec)

Typical Bear Rate (kbps)

BLER


Parameter Determining

The basic parameters in the service pattern 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.


NBLER

BLERNBLERNBLERNBLERNN n *1

1**** 32

PS Service Pattern Parameters

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:


User behaviour

PS User Behaviour Parameters

Penetration Rate

BHSA

User Distribution (High, Medium,

Low end)


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.


PS Traffic Model Parameters 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

)/()/(

)(

SessionNumPacketCallPacketCallPacketNum

PacketSizefficVolumeSessionTra


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

PS Traffic Model Parameters

)3600

(_

orActiveFactredRateTypicalBea

nEviromentApplicatioderTypicalroughputUnBusyHourThgRatePenetratinUserOfDiffrentPercentageErlangData


NotherownTOT PIII

Uplink Interference Analysis—Uplink Interference Composition

： Interference from the users of this cell

： Interference from users of adjacent cell

： Noise floor of the receiver

ownIotherI

NP


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.



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


Uplink Interference Analysis—Uplink Interference Composition Uplink Interference Analysis—Uplink Interference Composition

： 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

jj vR

W

PI

PNoEb

1/

N

jown PI1

jjj

TOTj

vRW

NoEb

IP

1/1

1

jTOT PI

jVjP

jP

ownI



: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


Define

Then

Uplink Interference Analysis

N

N

jjj

TOT

NotherownTOT

P

vRW

NoEb

Ii

PIII

1 1

/1

11

jjj

j

vRW

NoEb

L1

/1

1

1

N

N

jTOTTOT PLiII 1

1


ObtainObtain

N

j

NTOT

LiPI

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 = 0.67

Adjacent cell

− interference factor

− i = 0.55

jV


Uplink Interference Analysis—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

vRW

EbvsNo

iLi11 11

1

111

TOTI


Uplink Interference Analysis—Load Factor and Interference

According to the above mentioned relationship, the noise will rise:

1

1 1

11 1

TOTN

N ULj

INoiseRise

P i L

50% Load — 3dB50% Load — 3dB60% 60% LoadLoad — 4dB — 4dB75% 75% LoadLoad — 6dB — 6dB


Uplink Interference Analysis—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.


Uplink Interference Analysis—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




Chapter 3 Downlink capacity analysisChapter 3 Downlink capacity analysis




NotherownTOT PIII

Downlink Interference Analysis—Downlink Interference Composition

： Interference from other downlink DCH of this cell

： Interference from the downlink DCH of adjacent cell

： Noise floor of the receiver

ownI

otherI

NP



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



:Interference from other 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



: 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. 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

jkTjother PLPI

1 ,

1

otherI



N

K

jkT

j

Tj

NotherownTOT

PPL

PPL

P

PIII

1 ,

11

Suppose the power control is ideal, we obtainSuppose the power control is ideal, we obtain

jjjTOT

j

j

j vR

W

I

PLP

EbvsNo1

ThenThen

jjTOTjj

jj PLIvW

REbvsNoP


BecauseBecause N

jCCHT PPP1

ThenThen

jN

K

jk

jTTj

N

jj

jCCH

N

K

jkT

j

Tj

N

jjj

jCCH

N

jjTOTjj

jCCHT

PLPPL

PLPPv

W

REbvsNoP

PPL

PPL

PPLv

W

REbvsNoP

PLIvW

REbvsNoPP

1 ,1

1 ,1

1

1

11



Resolve PT to obtainResolve PT to obtain

N

jj

jjj

N

jjj

jNCCH

T

vW

REbvsNoi

PLvW

REbvsNoPP

P

1

1

11

wherewhere i ijj is the adjacent cell interference factor of the useris the adjacent cell interference factor of the user, ,

defined as:defined as:

K

jk

jj PL

PLi

1 ,



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

jj

jjjDL vW

REbvsNoi

1

1

ja ji


Downlink Interference Analysis—Simulation Result


Downlink Interference Analysis—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.





Chapter 4 Multi-service capacity estimationChapter 4 Multi-service capacity estimation




4.1 Network capacity restriction factors4.1 Network capacity restriction factors

4.2 Typical capacity design methods 4.2 Typical capacity design methods


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


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


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


Downlink Channel Code Resources

Following is an example of code Following is an example of code resources allocationresources allocation

SF 4 8 16 32 64 128 256 512 ┏ ━ ●C(256, 0): PCPI CH 2 ┏ 0 ┫ ┃ ┗ ━ ●C(256, 1): PCCPCH 3 ┏ 0 ┫ ┃ ┃ ┏ ━ ●C(256, 2): AI CH 6 ┃ ┗ 1 ┫ ┃ ┗ ━ ●C(256, 3): PI CH 10 ┏ 0 ┫ ┃ ┗ ━ ●C(64, 1): SCCPCH 8 ┏ 0 ┫ ┃ ┃ ┏ ━ ●C(64, 2): SCCPCH 9 ┃ ┗ 1 ┫ ┃ ┗ ━ ○3 ┏ 0 ┫ ┃ ┗ ━ ○1 ┏ 0 ┫ ┃ ┗ ━ ○1 ┃ ┗ ━ ○1

┏ ━ ○2 ┃ ┗ ━ ○3


Channel Element (CE) The Channel element is 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


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


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


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.



4.1 Network capacity restriction factors 4.1 Network capacity restriction factors

4.2 Typical capacity design methods4.2 Typical capacity design methods


Erlang-B Formula (I)

The Erlang-B formula is used for estimating

the peak traffic that meets certain call loss

rate when the average traffic (Erlang) is

given.

The Erlang-B formula is only used for

Circuit switched services

Single service

The WCDMA system provides CS and PS

domain multi-services


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


Post Erlang-B (I)

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


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


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.


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.


Equivalent Erlangs (II) Consider that two services share resources



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!

The predication results are not unique!


ia

Campbell’s Theorem (I) 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.

cfficOfferedTra

c

aCCapacity ii )(

ii

ii

aErlangs

aErlangsv

c

2


Campbell’s Theorem (II) Consider that two services share resources



The system mean value is

The system variance is

The capacity factor c is

3063121 iaErlangs

2.23066

vc

6636112 222 iaErlangsv


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.

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


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.


Network estimation procedure

Cell radius

User density

Service message

Compareover

Yes

No

Assumption of cell load and carrier number

Cell area Number of user per

cell

Balance between capacityand coverage dimension?

Uplink & downlink capacity dimension

Adjustment of cell load and carrier number

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05 OWJ100102 WCDMA Radio Network Capacity Planning (With Comment) ISSUE 1.0

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Transcript of 05 OWJ100102 WCDMA Radio Network Capacity Planning (With Comment) ISSUE 1.0