004 wcdma radio network capacity planning issue 1
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www.huawei.com
Internal
OWJ100102 WCDMARadio Network Capacity
Planning
ISSUE 1.0
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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
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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
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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
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ChapterChapter 11 TrafficTraffic ModelModel
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
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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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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
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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.
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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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System Configuration
User behaviour
Service Pattern
Traffic ModelResults
The Contents of Traffic Model
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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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ChapterChapter 11 TrafficTraffic ModelModel
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
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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)
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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
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ChapterChapter 11 TrafficTraffic ModelModel
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
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PS Traffic Model The most frequently used model is the packet service session
process model described in ETSI UMTS30.03.
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PS Traffic Model
Data Burst Data Burst Data Burst
Packet Call
Session
Packet Call Packet Call
Downloading Downloading
Active Dormant Dormant Active
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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)
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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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N BLER
BLER N BLER N BLER N BLER N N n *
1
1****
32
−
=+++++ LL
PS Traffic Model 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 sourceis 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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User behaviour
PS User Behaviour Parameters
User Distribution (High, Medium, Lowend)
BHSA
Penetration Rate
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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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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 =
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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 =
PS Traffic Model Parameters
)3600
(_ ∑⋅⋅
⋅⋅=
or ActiveFact redRateTypicalBea
nEviroment ApplicatioderTypicalroughputUn BusyHourThgRatePenetratinUser OfDiffrent Percentage Erlang Data
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ChapterChapter 11 TrafficTraffic ModelModel
ChapterChapter 22 UplinkUplink capacitycapacity analysisanalysis
ChapterChapter 33 DownlinkDownlink capacitycapacity analysisanalysis
ChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation
ChapterChapter 5 Network estimation5 Network estimation procedureprocedure
ChapterChapter 66 CapacityCapacity enhancementenhancement technologiestechnologies
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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
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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.
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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 −=+=
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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
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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
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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
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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
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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
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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
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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.
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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
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ChapterChapter 11 TrafficTraffic ModelModel
ChapterChapter 22 UplinkUplink capacitycapacity analysisanalysis
ChapterChapter 33 DownlinkDownlink capacitycapacity analysisanalysis
ChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation
ChapterChapter 5 Network estimation5 Network estimation procedureprocedure
ChapterChapter 66 CapacityCapacity enhancementenhancement technologiestechnologies
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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
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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 −=+=
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Downlink Interference Analysis—DownlinkInterference 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 channeltransmitting power
( ) ( )1 T own j j
j
P I
PLα = − ⋅
∑+=
N
jCCH T PPP1
own I
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Downlink Interference Analysis—DownlinkInterference Composition
: 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
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Downlink Interference Analysis—DownlinkInterference Composition
( ) 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 ⋅⋅⋅⋅=
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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
Downlink Interference Analysis—DownlinkInterference Composition
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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—DownlinkInterference Composition
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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
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Downlink Interference Analysis—Simulation Result
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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.
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ChapterChapter 11 TrafficTraffic ModelModel
ChapterChapter 22 UplinkUplink capacitycapacity analysisanalysis
ChapterChapter 33 DownlinkDownlink capacitycapacity analysisanalysis
ChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation
ChapterChapter 5 Network estimation5 Network estimation procedureprocedure
ChapterChapter 66 CapacityCapacity enhancementenhancement technologiestechnologies
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ChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation
4.1 Network capacity restriction factors4.1 Network capacity restriction factors
4.2 Typical capacity design methods4.2 Typical capacity design methods
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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
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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
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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
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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
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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
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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
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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
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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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ChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation
4.1 Network capacity restriction factors4.1 Network capacity restriction factors
4.2 Typical capacity design methods4.2 Typical capacity design methods
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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
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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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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
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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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Post Erlang-B overestimates the 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 rateTotal 31 unit resources
However, the reasonable results should be: 18 Erlangs require 26connections for meeting the 2% blocking rate.
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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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Equivalent Erlangs (II) Consider that two services share resources
Service 1: 1 unit resource/connection.12 Erlang
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!
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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
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Campbell’s Theorem (II) Consider that two services share resources
Service 1: 1 unit resource/connection.12 Erlang
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)
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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
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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.
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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.
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ChapterChapter 11 TrafficTraffic ModelModel
ChapterChapter 22 UplinkUplink capacitycapacity analysisanalysis
ChapterChapter 33 DownlinkDownlink capacitycapacity analysisanalysis
ChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation
ChapterChapter 5 Network estimation5 Network estimation procedureprocedure
ChapterChapter 66 CapacityCapacity enhancementenhancement technologiestechnologies
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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
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ChapterChapter 11 TrafficTraffic ModelModel
ChapterChapter 22 UplinkUplink capacitycapacity analysisanalysis
ChapterChapter 33 DownlinkDownlink capacitycapacity analysisanalysis
ChapterChapter 44 MultiMulti--serviceservice capacitycapacity estimationestimation
ChapterChapter 5 Network estimation5 Network estimation procedureprocedure
ChapterChapter 66 CapacityCapacity enhancementenhancement technologiestechnologies
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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
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Transmission Diversity-
TxDiv Gain of TxDiv
The gain is obtained due to additional amplifier
Pure gain is obtained due to TxDiv technology
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Transmission Diversity-
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.
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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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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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