03 OWJ100102 WCDMA Radio Network Capacity Planning (With Comment) ISSUE 1

www.huawei.com

Copyright © 2006 Huawei Technologies Co., Ltd. All rights reserved.

WCDMA RadioNetwork CapacityPlanning


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


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

� Grasp the parameters of 3G traffic model

� Understand the factors that restrict the WCDMA networkcapacity

� Understand the methods and procedures of estimating multi-service capacity

� Understand the key technologies for enhancing networkcapacity


Contents1. Traffic Model

2. Uplink capacity analysis

3. Downlink capacity analysis

4. Multi-service capacity estimation

5. Network estimation procedure

6. Capacity enhancement technologies



1.1 Overview of traffic model

1.2 CS traffic model

1.3 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 rate

symmetry

Background

download of

Email

Background

Web page

browse,

network gameInteractive

Non real-tim

e category

Streaming

multimediaStreaming

Voice service,

videophoneConversational

Real-tim

e category


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 willgenerate different system capacities

� We need to set up an expected data transmission model ofthe customer so that we can plan the network properly

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


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 CallDownloading 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


NBLER

BLERNBLERNBLERNBLERNN n *1

1**** 32

−=+++++ ΛΛ


� 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

User Distribution (High, Medium, Low

end)

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 servicefor purpose of transmitting data.

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

eTypicalRatfficVolumeSessionTra

BLERsissionTimeDataTransm 1000/8**

11)(

−=

)(Re*)1/(

sissionTimeDataTransmadingTimeSessionlNumPackketCaleHoldingTim

+−=

)/(*)/(*)( SessionNumPacketCallPacketCallPacketNumPacketSizefficVolumeSessionTra =


� 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)

eHoldingTimissionTimeDataTransmorActiveFact =

1000/8**/ fficVolumeSessionTraBHSAuseroughputBusyHourTh =


)3600

(_ ∑ ⋅⋅⋅⋅=

orActiveFactredRateTypicalBeanEviromentApplicatioderTypicalroughputUnBusyHourThgRatePenetratinUserOfDiffrentPercentageErlangData


NotherownTOT PIII ++=

Uplink Interference Analysis—UplinkInterference 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 bandwidth3.84MHz

� 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—UplinkInterference CompositionUplink Interference Analysis—UplinkInterference 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

WPI

PNoEb 1/ ⋅⋅

−=

∑=N

jown PI1

( ) jjj

TOTj

vRW

NoEb

IP 1/11 ⋅⋅+

=

jtotal PI −

jVjP

jP

ownI



� :Interference from users of adjacent cell

� The interference from users of adjacent cell is difficult to analyzetheoretically, 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 cellinterference factor is 0.65

own

other

IIi =

otherI


Define

Then

Uplink Interference Analysis

( )( )

N

N

jjj

TOT

NotherownTOT

P

vRW

NoEb

Ii

PIII

+⋅⋅+

+=

++=

∑1 1

/11

1

( ) jjj

j

vRW

NoEb

L 1/11

1

⋅⋅+=

( ) N

N

jTOTTOT PLiII +⋅+⋅= ∑1

1


ObtainObtain( ) ∑⋅+−

⋅= N

j

NTOT

LiPI

111

1

Uplink Interference Analysis

� Suppose that:

� All the users are 12.2 kbps voiceusers, the demodulation thresholdEb/No = 5dB

� Voice activation factor vj = 0.67

� Adjacent cell� interference factor

� i = 0.55


Uplink Interference Analysis—UplinkLoad 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 111

111η

TOTI


Uplink Interference Analysis—LoadFactor and Interference� According to the above mentioned relationship, the

noise will rise:( )

1

1 111 1

TOTN

N ULj

INoiseRiseP i L η

= = =−− + ∑

50% Load50% Load —— 3dB3dB60%60% LoadLoad —— 4dB4dB75%75% LoadLoad —— 6dB6dB


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 isslightly 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. (Butcall 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 complicatethe calculation process.

� Since the time-variable feature of the mobile transmission environment, thedemodulation 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, andreduces the system capacity

� Assume that the users are distributed evenly, and theadjacent cell interference is constant

� Considering the above factors, the system simulation is amore accurate method:

� Static simulation: Monte_Carlo method

� Dynamic simulation


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 −=+=


Downlink Interference Analysis—Downlink Interference Composition� :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

PIPL

α= − ⋅

∑+=N

jCCHT PPP1

ownI



� : 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 thescrambling codes of users are different, such interference isnon-orthogonal

� Assume the service is distributed evenly, the transmittingpower of all BTSs will be equal. k,j In the system, there are Kadjacent cell BTSs, where path loss from the number k BTSto the user j is PLk,j. Hence we obtain:

( ) ∑⋅=K

jkTjother PLPI

1 ,

1

otherI



( ) N

K

jkT

j

Tj

NotherownTOT

PPL

PPLPPIII

+⋅+⋅−=

++=

∑1 ,

11 α

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

( ) ( ) jjjTOT

j

j

j vRW

IPL

P

EbvsNo 1⋅⋅=

ThenThen

( ) ( ) jjTOTjj

jj PLIvWR

EbvsNoP ⋅⋅⋅⋅=


BecauseBecause ∑+=N

jCCHT PPP1

ThenThen

( ) ( )

( ) ( )

( ) ( )

⋅+⋅+⋅−⋅

⋅⋅+=

+⋅+⋅−⋅

⋅⋅⋅+=

⋅⋅⋅⋅+=

∑∑

∑∑

∑

jN

K

jk

jTTj

N

jj

jCCH

N

K

jkT

j

Tj

N

jjj

jCCH

N

jjTOTjj

jCCHT

PLPPLPL

PPvWR

EbvsNoP

PPL

PPLPPLv

WR

EbvsNoP

PLIvWR

EbvsNoPP

1 ,1

1 ,1

1

1

11

α

α



Resolve PT to obtainResolve PT to obtain

( )

( ) ( )∑

∑

⋅⋅⋅+−−

⋅⋅⋅⋅+

=N

jj

jjj

N

jjj

jNCCH

T

vWR

EbvsNoi

PLvWR

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

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 isinfinite, and the corresponding capacity is called “threshold capacity”.

� As different from the theoretic calculation of uplink capacity, and inthe downlink capacity formula are variable related to user position. Namely,the downlink capacity is related to the spatial distribution of the users, andcan only be determined through system simulation.

( ) ( )∑

⋅⋅⋅+−=

N

jj

jjjDL vWR

EbvsNoi1

1 αη

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.


Contents4. Multi-service capacity estimation

4.1 Network capacity restriction factors

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, andthe other part for dedicated (traffic) channel.

� The transmit power is allocated by the cellto each user varies with servicedemodulation threshold, propagation pathloss and the interference received by theuser

� The downlink transmit power of the cell isshared by all the users in the cell

� We generally use the simulation method toanalyze the downlink interference.

∑+=N

jCCHT PPP1


Downlink Channel Code Resources� The WCDMA network use the codes

whose SF is 4~512. The smaller theSF is, the higher the supported datarate will be.

� In the code tree, the allocable codesshould meet the following conditions:

� No codes on the path from thiscode to the root node of code treeare allocated

� No codes in the sub-tree whoseroot node is this code are allocated

� Try to reserve the code wordswhose SF is small, so as toimprove 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 ResourcesFollowing is an example of code resources allocationFollowing 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 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:

� 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


Contents4. Multi-service capacity estimation

4.1 Network capacity restriction factors

4.2 Typical capacity design methods


Erlang-B Formula (I)

� Erlang-B formula is used forestimating the peak traffic thatmeets certain call loss rate whenthe average traffic (Erlang) is given

� Erlang-B formula is only used for

� Circuit switched services

� Single service

� The WCDMA system provides CSand 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, itsvariance is equal to its mean value

� If, when a service establishes a link, the service requiresthe resources which are more than the unit resources, theresource request is no longer equal to its mean value, andthe 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 overestimatesthe capacity requirements!

Post Erlang-B (III)� Consider that two services use the same resources

� Service 1: 1 unit resource/connection.12 Erlang� Service 2: 1 unit resource/connection.6 Erlang

� 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 Erlangsrequire 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 servicesare 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 servicesare 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 resultsare not unique!


ia

Campbell’s Theorem (I)� The Campbell theorem sets up a combined distribution

� Here:

� is service amplitude, namely, the channel resourcesrequired for a single link of the service.

� is the mean value, v is the variance.

cfficOfferedTra α=

caCCapacity ii )( −=

∑∑

×

×==

ii

ii

aErlangs

aErlangsvc

2

α

α


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



� The system mean value is

� The system variance is

� The capacity factor c is 1

3063121 =×+×=×=∑ iaErlangsα

2.23066 === α

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 capacityrequired 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 ===c

fficOfferedTra α


The comparison of the differentcapacity 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.


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 to

calculate the capacity is described


Network estimation procedure

Cellradius

User density

Service message

Compareover

Yes

No

Assumption of cellload 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 additional antenna


Transmission Diversity －TxDiv

� Gain of TxDiv

� The gain is obtained due to additional amplifier

� Pure gain is obtained due to TxDiv technology


Transmission Diversity －TxDiv� Gain of TxDiv

� The gain is obtained due toadditional amplifier

� Pure gain is obtained due toTxDiv technology

� TxDiv should reduce downlinkpower

� TxDiv should reducerequirement of Eb/N0

� Usually ,closed loop TxDivwould obtain more gain thanopen loop TxDiv


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%


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 youwww.huawei.com

03 OWJ100102 WCDMA Radio Network Capacity Planning (With Comment) ISSUE 1

Documents

Transcript of 03 OWJ100102 WCDMA Radio Network Capacity Planning (With Comment) ISSUE 1