004 wcdma radio network capacity planning issue 1

7/28/2019 004 WCDMA Radio Network Capacity Planning ISSUE 1

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OWJ100102 WCDMARadio Network Capacity

Planning

ISSUE 1.0




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 certaintraffic 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

Understand the key technologies for enhancing

network capacity




ChapterChapter 11 TrafficTraffic ModelModel

ChapterChapter 22 UplinkUplink capacitycapacity analysisanalysis

ChapterChapter 33 DownlinkDownlink capacitycapacity analysisanalysisChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation

ChapterChapter 5 Network estimation5 Network estimation procedureprocedure

ChapterChapter 66 CapacityCapacity enhancementenhancement technologiestechnologies





1.1 Overview of traffic model1.1 Overview of traffic model

1.2 CS traffic model1.2 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

Data integrity should be maintained. Small delay

restriction, requiring correct transmission

Request-response mode, data integrity must be

maintained. High requirements on error tolerance,

low requirements on time delay tolerance

Typically unidirectional services, high requirements

on error tolerance, high requirements on data rate

It is necessary to maintain the time relationship

between the information entities in the stream.

Small time delay tolerance, requiring data ratesymmetry .

Background

download of

Email.

Background

Web page

browse,

network gameInteractive

N onr e al - t i m e c

a t e g or y

Streaming

multimediaStreaming

Voice service,

videophoneConversational

R e a

l - t i m e c a t e g or y




Objectives of Setting Up Traffic Model

In order to determine the system configuration, we need todetermine 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

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.




System Configuration

User behaviour

Service Pattern

Traffic ModelResults

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




CS Traffic Model

Voice service is a typical CS services. Voice data

arrival conforms to the Poisson distribution. Its timeinterval 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 * meanrate

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

mean busy hour throughput per user * 1000/3600




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

process model described in ETSI UMTS30.03.




PS Traffic Model

Data Burst Data Burst Data Burst

Packet Call

Session

Packet Call Packet Call

Downloading Downloading

Active Dormant Dormant Active




Traffic model

PS Traffic Model Parameters

Packet Call Num/Session

Packet Num/Packet Call

Packet Size (bytes)

BLER

Typical Bear Rate (kbps)

Reading Time (sec)




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.




N BLER

BLER N BLER N BLER N BLER N N n *

1

1****

32

−

=+++++ LL


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 sourceis 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

User Distribution (High, Medium, Lowend)

BHSA

Penetration Rate




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

BLERsissionTime DataTransm

1000 / 8**

1

1)(

−

=

)(

Re*)1 / (

sissionTime DataTransm

adingTimeSessionlNumPackketCal

e HoldingTim

+

−

=

/ (*) / (*)( Sessio NumPacketCallPacketCallPacketNumPacketSize fficVolumeSessionTra =




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)

e HoldingTim

issionTime DataTransmor ActiveFact =

1000 / 8** / fficVolumeSessionTra BHSAuser roughput BusyHourTh =


)3600

(_ ∑⋅⋅

⋅⋅=

or ActiveFact redRateTypicalBea

nEviroment ApplicatioderTypicalroughputUn BusyHourThgRatePenetratinUser OfDiffrent Percentage Erlang Data






ChapterChapter 33 DownlinkDownlink capacitycapacity analysisanalysis

ChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation






N other ownTOT P I I I ++=

Uplink Interference Analysis—Uplink InterferenceComposition

：Interference from the users of this cell

： Interference from users of adjacent cell

：Noise floor of the receiver

own I

other I

N P




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 downlinkperspectives.




Uplink Interference Analysis—Uplink Interference Composition

Receiver noise floor PN

− K：Boltzmann constant, 1.38×

− T：Kelvin temperature, normal temperature: 290 K

− W：Signal bandwidth, WCDMA signal bandwidth3.84MHz

− 10lg(KTW) = -108dBm/3.84MHz

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

NF W T K P N += )**log(10

K J / 10 23−

MHzdBm NF W T K P N 84.3 / 105)**log(10 −=+=




Uplink Interference Analysis—Uplink Interference CompositionUplink 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:

( ) j j jTOT

j

jv R

W

P I

P No Eb

1 / ⋅⋅

−

=

∑=

N

jown P I

1

( ) j j j

TOT j

v RW

No Eb

I P

1 / 11 ⋅⋅+

=

jtotal P I −

jV jP

jP

own I




Uplink Interference Analysis—Uplink Interference Composition

: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, andantenna 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

I i =

other I




Define

Then

Uplink Interference Analysis

( )

( )

N

N

j j j

TOT

N other ownTOT

P

v RW

No Eb

I i

P I I I

+

⋅⋅+

+=

++=

∑1 1

/ 11

1

( ) j j j

j

v RW

No Eb

L

1 / 11

1

⋅⋅+

=

( ) N

N

jTOT TOT P Li I I +⋅+⋅= ∑1

1




ObtainObtain

( ) ∑⋅+−

⋅= N

j

N TOT

Li

P I

111

1

Uplink Interference Analysis

Suppose that:

All the users are 12.2 kbps voice

users, the demodulation thresholdEb/No = 5dB

Voice activation factor vj = 0.67

Adjacent cell

− interference factor

− i = 0.55




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

j j j

N

jUL

v R

W

EbvsNo

i Li11

111

111η

TOT I




Uplink Interference Analysis—Load Factor andInterference

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

( )1

1 1

11 1

TOT

N

N UL j

I

NoiseRise Pi L

η = = =−

− + ∑

50% Load50% Load —— 3dB3dB

60%60% LoadLoad —— 4dB4dB

75%75% LoadLoad —— 6dB6dB




Uplink Interference Analysis—Limitation of theCurrent 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 themgenerate 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 theCurrent 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




N other ownTOT P I I I ++=

Downlink Interference Analysis—DownlinkInterference Composition

：Interference from other downlink DCH of this cell

：Interference from the downlink DCH of adjacent cell

：Noise floor of the receiver

own I other I

N P




Downlink Interference Analysis—Downlink

Interference Composition

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）

NF W T K P N

+= )**log(10

K J / 10 23−

MHzdBm NF W T K P N 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 channeltransmitting power

( ) ( )1 T own j j

j

P I

PLα = − ⋅

∑+=

N

jCCH T PPP1

own I





: Interference from the downlink DCH of adjacent cell

The transmitting signal of the adjacent cell BTS will causeinterference 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

T jother PL

P I 1 ,

1

other I





( ) N

K

jk

T

j

T j

N other ownTOT

P

PL

P

PL

P

P I I I

+⋅+⋅−=

++=

∑1 ,

11 α

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

( )( ) j j jTOT

j

j

jv R

W

I

PL

P

EbvsNo1

⋅⋅=

ThenThen

( ) ( ) j jTOT j

j

j j PL I vW

R EbvsNoP ⋅⋅⋅⋅=




BecauseBecause ∑+=

N

jCCH T PPP1

ThenThen

( ) ( )

( ) ( )

( ) ( )

⋅+⋅+⋅−⋅

⋅⋅+=

+⋅+⋅−⋅

⋅⋅⋅+=

⋅⋅⋅⋅+=

∑∑

∑∑

∑

j N

K

jk

jT T j

N

j j

jCCH

N

K

jk

T

j

T j

N

j j

j

jCCH

N

j jTOT j

j

jCCH T

PLPPL

PLPPv

W

R EbvsNoP

PPL

PPL

PPLv

W

R EbvsNoP

PL I v

W

R EbvsNoPP

1 ,1

1 ,1

1

1

11

α

α





Resolve PT to obtainResolve PT to obtain

( )

( ) ( )∑

∑

⋅⋅⋅+−−

⋅⋅⋅⋅+

= N

j

j

j j j

N

j j

j

j N CCH

T

vW

R EbvsNoi

PLvW

R

EbvsNoPPP

1

1

11 α

wherewhere iijj is the adjacent cell interference factor of the useris the adjacent cell interference factor of the user,,

defined as:defined as:

∑=K

jk

j

jPL

PLi1 ,





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

j j j DL vW

R EbvsNoi

1

1 α η

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.





4.1 Network capacity restriction factors4.1 Network capacity restriction factors

4.2 Typical capacity design methods4.2 Typical capacity design methods




Capacity Restriction Factors

The WCDMA network capacity restriction factorsin 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

jCCH T 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 treeare allocated

No codes in the sub-tree whose

root node is this code are allocated

Try to reserve the code wordswhose 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 resources allocation 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




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 relatedto 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:

AMR12.2kbps 1

CS64kbps 4

CS144kbps 8

CS384kbps 16

PS64kbps 4

PS144kbps 8

PS384kbps 16




Iub Interface Capacity

The contents transmitted on the Iub interface

include:

The user data encapsulated in the AAL2

format (common channel and dedicated

channel)

Signaling 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 factors4.1 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（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




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 rateTotal 31 unit resources

However, the reasonable results should be: 18 Erlangs require 26connections 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


Service 2: 3 unit resources/connection.6 Erlang

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 unitresources of service 1), meeting the 2% blocking rate

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.

c fficOfferedTra

α =

c

aC

Capacity

ii )(=

∑

∑×

×

==

i

i

i

i

a Erlangs

a Erlangsv

c

2

α




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


Service 2: 3 unit resources/connection.6 Erlang The system mean value is

The system variance is

The capacity factor c is 1

3063121 =×+×=×= ∑ ia Erlangsα

2.23066

===α

vc

6636112 222=×+×=×= ∑ ia Erlangsv

C b ll’ Th (III)




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 =47Goal 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===

c fficOfferedTra




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 arerequired

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.




Summary of This Chapter

This chapter deals with the three methods of estimating

the multi-service capacity.

The detailed process of using the Campbell theorem tocalculate the capacity is described.




Network estimation procedure

Cellradius

User density

Service message

Compareover

Yes

No

Assumption of cell

load and carriernumber

Cellarea Number of user percell

Balance between capacitydimensionand coverage dimension ?

Uplink capacity dimension ,downlink capacitydimension

Adjustment of cellload and carriernumber




Transmission Diversity－

TxDiv

Txdiv has two types in

WCDMA system:

Open loop TxDiv

Closed loop TxDiv

TxDiv could improve

downlink capacity

Need additional amplifier

Need equipment support

Don’t need additionalantenna





TxDiv Gain of TxDiv

The gain is obtained due to additional amplifier

Pure gain is obtained due to TxDiv technology





TxDiv Gain of TxDiv

The gain is obtained due to

additional amplifier

Pure gain is obtained due to

TxDiv technology

TxDiv should reduce downlink

power TxDiv should reduce requirement of

Eb/N0

Usually ,closed loop TxDiv would

obtain more gain than open loopTxDiv.





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%




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



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004 wcdma radio network capacity planning issue 1

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