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8/13/2019 GSM-To-UMTS Training Series 03_WCDMA Radio Network Capacity Dimensioning_V1.0
1/93
HUAWEI TECHNOLOGIES CO., LTD.
www.huawei.com
HUAWEI Confidential
Security Level:
WCDMA Radio Network
Capacity Dimensioning
GSM-to-UMTS Training Series_V1.0
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Revision Record
Date Revision
Version
Description Author
2008-10-31 1.0 Draft completed. Zang Liang
2008-12-31 1.1 Added comparison of the capacity dimensioning of
the GPRS and WCDMA to Page 3.
Explained the formula for calculation of the data
volume on Page 23.
Explained BHSA on Page 24.Explained the related units on Page 34.
Explained the formula and parameters on Page 35.
Described the downlink interference on Page 57.
Zhang Bibo
2009-01-15 1.2 Added remarks on Pages 3, 16, and 79 to compare
capacity dimensioning of a GSM network with that
of a WCDMA network.
Hou Chong
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Capacity Features: WCDMA vs GSM
WCDMA is a self-interference system.
The WCDMA system capacity is closely related to coverage.
The WCDMA network capacity has the soft capacity feature.
The capacity dimensioning of a WCDMA network is based on
a certain traffic model.
Interference in the GSM network is mainly from the cells that use the same
frequency. There is negligible interference from the subscribers in the same cell.
In the GSM system, the capacity is independent of the coverage.
The GSM system capacity is relatively fixed and can be estimated based on the
frequency and the number of timeslots.
The uplink capacity of the GSM network is the same as the downlink capacity.
The capacity dimensioning of a GSM network is based on a certain traffic model.
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Foreword
At the early stage of radio network planning, we must aim to know the
scale of the network. For example, we must know how many devices
are required and how to configure the devices. Based on coverage
estimation, we can calculate the cell radius under a specific load by
using the path loss, and then obtain the number of subscribers under
the cell coverage in combination with the subscriber density.
In actual planning, can we obtain the cell radius only by considering
the coverage? Should other aspects be taken into consideration?
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Objectives
Understand the parameters of 3G traffic model
Understand the factors that restrict the
WCDMA network capacity
Understand the methods and procedures for
estimating multi-service capacity
Understand the key technologies for
enhancing network capacity
Upon completion of this course, you will be able to:
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Contents1. Traffic Model
2. Uplink capacity analysis
3. Downlink capacity analysis
4. Multi-service capacity estimation
5. Network estimation procedure
6. Capacity enhancement technologies
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1.TrafficModel
1.1 Overview o f Traff ic Model
1.2 CS Traffic Model
1.3 PS Service Model
1.4 PS Traffic Model Parameters
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Service Overview
The WCDMA system supports multiple services.
Variable-rate services
Hybrid services
High-speed data packet services
Asymmetrical services
Large-capacity and flexible service bearing
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QoSType
Real-time
category
Session
It is necessary to maintain the time relationship between
the information entities in the stream. Low delay toleranceand symmetric data rate are required.
Voice
service,video
phone,
video
game
Streaming
It is necessary to maintain the time relationship between
the information entities in the stream. Unidirectionalservices, high error tolerance, and high data rate are
required.
Streaming
multimedia
Non real-time
category
Interactive
Data integrity must be maintained in request-response
mode. High error tolerance and low delay tolerance are
required.
Web page
browsing,
online
game
Background
The information receiving end does not expect the data
arrival within a period of time. Data integrity should be
maintained. Small delay restriction and error-free
transmission are required.
Backgroun
d download
of Email
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Objectives of Establishing Traffic Models
To determine the system configuration, calculate the call capacity of air
interfaces.
To determine the system capacity. In data applications, different
transmission models lead to different system capacities.
-To plan the network properly by establishing a data transmission model
that meets the customers expectations.
Traffic models are provided by telecom operators. If a telecom operator
cannot provide the traffic model, a traffic model for similar scenarios or
a traffic model recommended by Huawei can be used as the basis for the
planning upon the approval of the telecom operator.
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TrafficModel
A traffic model deals with the capacity features of each service
type and the QoS expected by the subscribers who are using
the service from the perspective of data transmission.
In data applications, the subscriber behavior research predicts
the service types available on a 3G network, number of
subscribers of each service type, usage frequency of the
service, and distribution of subscribers in different regions.
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System Configuration
User Behavior
Service Model
Traffic Model
Results
Contents of Traffic Model
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Typical Service Features
Typical service features include the following featureparameters:
Subscriber type (such as indoor, outdoor, and inside a
vehicle)
Average motion speed of a subscriber
Service type
Uplink and downlink service rates
Spreading factor (SF)
Time delay requirements of the service
QoS requirements of the service
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1. Traffic Model
1.1 Overview of Traffic Model
1.2 CS Traff ic Mo del
1.3 PS Service Model
1.4 PS Traffic Model Parameters
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CS Traffic Model
A typical CS service is the voice service. The arrival of the CS
subscribers takes on a Poisson distribution, while the interval
takes on a negative exponential distribution.
Related 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 per user (Erlang) = (BHCA x Mean call
duration)/3600 (Erl)
Mean busy-hour throughput per user (kbit) (G) = BHCA x Mean call
duration x activation factor x Mean rate of service (kbps)
Mean busy-hour throughput per user (bps) (H) = (Mean busy-hour
throughput per user x 1000)/3600
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1. Traffic Model
1.1 Overview of Traffic Model
1.2 CS Traffic Model
1.3 PS Service Model
1.4 PS Traffic Model Parameters
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PS Traffic Model
The most frequently used model is the packet service session process
model described in ETSI UMTS30.03.
PS Services
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PS Services
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1.TrafficModel
1.1 Overview of Traffic Model
1.2 CS Traffic Model
1.3 PS Service Model
1.4 PS Traff ic Model Parameters
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Service Model
PSServiceModelParameters
Packet Call Num/Session
Packet Num/Packet Call
Packet Size (bytes)
Reading Time (sec)
Typical Bearer Rate (kbps)
BLER
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Determining Parameters
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 by
processing the sample data.
Take the distribution most proximate to the standard probability
as the corresponding parameter distribution through comparison
between the obtained distribution and the standard distribution
function.
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Typical Bearer Rate (kbit/s):
The bearer rate varies during the actual transmission.
BLock Error Rate (BLER):
For the PS domain service, we need to consider the retransmission
caused by error blocks when calculating the data transmission time.
Assume that the data volume of a service source is N and that the
block error rate at the air interface is BLER. The total data volume to be
transmitted on the air interface is as follows:
PSTrafficModelParameters
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User behavior
Penetration Rate
BHSA
User Distribution (High, Medium,
Low end)
PS User Behavior Parameters
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PS User Behavior Parameters
Penetration Rate
The percentage of the users that activates this service to all the
users registered in the network.
BHSA The number of sessions of single-user in busy hour
specific to a 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 scenarios have different user
distributions.
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Session traffic volume (Byte):Average traffic of single session of the
service
Session traffic volume = Packet size x Packet number per packet call x
Packet call number per session
Data transmission time (s):Transmission time for a single session of
service
Data transmission time = (1/(1-BLER)) x (((Session traffic volume x
8)/1000)/Typical bearer rate)
Holding time (s):Average duration of a single session of service
Holding time = (Packet call number per session -1) x Reading time + Data
transmission time
Derivative Parameters of the PS Traffic Model
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Activation factor: The proportion of the time when services transmits
at full-rate in the duration of a single session.
Busy hour throughput per user (Kbit):
Derivative Parameters of the PS Traffic Model
timeHolding
timeontransmissiDatafactorActivation
1000/)8**(/ VolumeTrafficSessionBHSAuserThroughputHourBusy
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Traffic Model Sample
Mobile
Video
(Stream) on
Demand
Penetration
RateBHSA
Busy Hour
Throughput/use
r (kbit)
Typical
Bearer
Rate
(kbps)
BLERActivation
Factor
Uplink 22.0% 0.100 2.304 8 10% 0.1798
Downlink 22.0% 0.100 102.528 64 10% 1.0000
Mobile
Video
(Stream) on
Demand
Packet Call
Number/Se
ssion
Packet
Number/P
acket Call
Packet Size
(Bytes)
Reading
Time
(sec)
Session
Traffic
Volume
(Byte)
Holding
Time
Uplink 2 3 480 14.6000 2880 17.8000
Downlink 1 267 480 0.0000 128160 17.8000
Data_Erlang = ((Proportion of subscribers at each level in typical application
environment x Penetration rate x Busy throughput per user in typical application
environment)/(Bearer rate x 3600 x Activation factor of this service))
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Questions
1. What are the two parts of the traffic model?
2. What are the main parameters of the CS traffic
model?
3. What are the main parameters of the PS service
model?
4. What is the formula for calculating the equivalent
Erlang of the PS service?
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Summary
Content of the traffic model
Main parameters of CS service traffic model
Structure, main parameters, and derivative parameters ofthe PS service model
Calculation of the equivalent Erlang of the PS services
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Contents1. Traffic Model
2. Uplink capacity analysis
3. Downlink capacity analysis
4. Multi-service capacity estimation
5. Network estimation procedure
6. Capacity enhancement technologies
Basic Principles
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Basic Principles
The radio system capacity depends on the uplink and downlink
capacities. When planning the capacity, analyze the uplink anddownlink.
In the WCDMA system, all the cells may share a same spectrum,
which greatly helps improve the system capacity. However, co-frequency reuse causes interference between subscribers. Such
multi-access interference restricts the system capacity.
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NotherownTOT PIII
Iown: interference from subscribers of this cell
Iother: interference from subscribers of adjacent
cells
PN: receiver noise floor
Uplink Interference AnalysisComposition of
Uplink Interference
Uplink Interference Analysis Composition of
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Receiver noise floor PNPN= 10lg (KTW) + NF
K: Boltzmann constant, 1.38 x 10-23 J/K
T: Kelvin temperature. The normal temperature is 290 K.
W: signal bandwidth. The WCDMA signal bandwidth is 3.84
MHz.
NF: noise factor of a receiver
10lg (KTW) = -108 dBm/3.84 MHz
NF = 3 dB (typical value of macro-cellular NodeB)
PN= 10lg (KTW) + NF = -105 dBm/3.84 MHz
Uplink Interference AnalysisComposition of
Uplink Interference
Uplink Interference AnalysisComposition of
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Iown: interference from subscribers of this cell
Interference to be overcome by each subscriber: ITOT- Pj
Pjis the receiving power of subscriber j
Under ideal power control:
Hence:
The interference from subscribers of this cell are the sum
of powers of all the subscribers arriving at the receiver:
jjjTOT
j
jvR
W
PI
PNoEb
1/
jjj
TOTj
vR
W
NoEb
IP1
/
11
N
jown PI1
p y p
Uplink Interference
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Iother: interference from subscribers of neighbor cells
It is difficult to theoretically analyze the interference from
subscribers of neighbor cells, because the interference is
related to subscriber distribution, cell layout, and antenna
pattern.
Interference factor of a neighbor cell:
When the subscribers are distributed evenly:
For an omni-directional cell, the typical interference factor
of neighbor cells is 0.55.
For a three-sector directional cell, the typical interferencefactor of neighbor cells is 0.65.
own
other
I
I
i
Uplink Interference AnalysisComposition of
Uplink Interference
Uplink Interference Analysis
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N
N
jjj
TOT
NotherownTOT
P
vR
W
NoEb
Ii
PIII
1
1
/
11
1
Define:
jjj
j
vR
W
NoEb
L1
/
11
1
Then, NN
jTOTTOT PLiII 1
1
Uplink Interference Analysis
Uplink Interference Analysis
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Obtain
N
j
NTOT
Li
PI
1
11
1
Assume that:
All the subscribers are 12.2kbit/s voice subscribers,
and the demodulation
threshold is Eb/No = 5 dB.
Voice activation factor vj=
0.67.
Interference factor of
neighbor cells i = 0.55.
Uplink Interference Analysis
Uplink Interference Analysis
U li k L d F t
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Uplink Load Factor
Define the uplink load factor
When the load factor is 1, ITOTis infinite and the corresponding
capacity is called threshold capacity.
Under this assumption, the threshold capacity is approximately 96
subscribers.
N
jjj
N
jUL
vR
W
Ebv sNo
iLi11
111
111
Uplink Interference Analysis
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Load Factor and Interference
Based on the previous formulas,
we can obtain the noise rise:
50% Load3 dB
60% Load4 dB
75% Load6 dB
Uplink Interference Analysis
Li it ti f th C t M th d
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In the preceding theoretical analysis, there are certain limitations as some
factors have not been taken into considerations and some assumptions have
been made.
No consideration of the influence of soft handover
The interference generated by the subscribers in soft handover state
are slightly lower than those generated by ordinary subscribers.
No consideration of the influence of AMRC and hybrid service
AMRC reduces the voice service rate of certain subscribers, reduces
the interference generated by these subscribers, and increases the
number of subscribers supported by the system at the cost of lowering
the call quality of these subscribers.
Different services have different data rates and demodulation thresholds.
The preceding method can still be used for analysis, but the calculation
process is complicated.
Due to the time-variable feature of the mobile transmission environment,
the demodulation threshold even for a same service is time-variable.
Limitations of the Current Method
Uplink Interference Analysis
Li i i f h C M h d
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Assumption of ideal power control
The power control commands of an actual system have certain error
codes. As a result, the power control process is not ideal, and the
system capacity is reduced.
Assumption that the subscribers are evenly distributed and the neighbor
cell interference is constant.
In consideration of the preceding factors, the system emulation is a more
accurate method:
Static emulation: Monte Carlo method
Dynamic emulation
Limitations of the Current Method
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Contents1. Traffic Model
2. Uplink capacity analysis
3. Downlink capacity analysis
4. Multi-service capacity estimation
5. Network estimation procedure
6. Capacity enhancement technologies
Downlink Interference Analysis
C iti f th D li k I t f
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NotherownTOT PIII Iown: interference from subscribers of this cell
Iother: interference from subscribers of adjacent cells
PN: receiver noise floor
Composition of the Downlink Interference
Downlink Interference Analysis
Composition of the Downlink Interference
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Receiver noise floor PN
PN= 10lg (KTW) + NF
K: Boltzmann constant, 1.38 x 10-23 J/K
T: Kelvin temperature. The normal temperature is 290 K.
W: signal bandwidth. The WCDMA signal bandwidth is
3.84 MHz.
NF: noise factor of a receiver
10lg (KTW) = -108 dBm/3.84 MHz
NF = 7 dB (typical value of UE)
PN= 10lg (KTW) + NF = -105 dBm/3.84 MHz
Composition of the Downlink Interference
Downlink Interference Analysis
Composition of Downlink Interference
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Iown: interference from this cell
Downlink subscribers are differentiated with the mutually
orthogonal OVSF codes. In static propagation conditions
without multi-path, no mutual interference exists.
In the case of multi-path propagation, certain energies that
cannot be detected by the RAKE receiver become interference
signals. The orthogonal factor
is defined to describe thisphenomenon.
In the formula, PT
indicates the total transmit power of a
NodeB, including the dedicated-channel transmit power
and the common-channel transmit power.
1 Town jjj
PI
PL
N
jCCHT PPP1
Composition of Downlink Interference
Downlink Interference Analysis
Composition of Downlink Interference
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Iother: interference from neighbor cells
The transmitting signals of neighbor cells cause interference to
the subscribers in the current cell. As the scrambling codes are
different, such interference is non-orthogonal.
Assume that the service is evenly distributed and the transmit
powers of all NodeBs are equal. The system consists of K
NodeBs in neighbor cells, and the path loss from NodeB k to
subscriber j is PLk,j. The interference from neighbor cells forsubscriber j is calculated as follows:
K
jk
TjotherPL
PI
1 ,
1
Composition of Downlink Interference
Downlink Interference Analysis
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NK
jk
T
j
Tj
NotherownTOT
PPL
PPLP
PIII
1 ,
11
Assume the power control is ideal, then:
jjjTOT
j
j
jvR
W
I
PLP
EbvsNo 1
The following formula can be obtained:
jjTOTjj
jj PLIvW
REbvsNoP
Downlink Interference Analysis
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Based on this
formula: N
jCCHT PPP
1
The following formula
can be obtained:
jN
K
jk
j
TTj
N
j
j
jCCH
N
K
jk
T
j
Tj
N
jj
j
jCCH
N
jjTOTj
j
jCCHT
PLP
PL
PLPPv
W
REbv sNoP
PPL
PPL
PPLv
W
REbv sNoP
PLIv
W
REbv sNoPP
1 ,1
1 ,1
1
1
11
Downlink Interference Analysis
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Calculate PTas follows:
N
j
j
jjj
N
jj
j
jNCCH
T
v
W
REbvsNoi
PLvW
REbvsNoPP
P
1
1
11
Where ijis the interference factor of neighbor cells of J,
assume that the following formula is true:
K
jk
jj
PLPLi
1 ,
Downlink Interference Analysis
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According to the preceding analysis, we can define the downlink load factor:
When the downlink load factor reaches 100%, the transmit power of the
NodeB is infinite, and the corresponding capacity is called threshold capacity.
Different from the theoretic calculation of uplink capacity, j and ijin the
downlink capacity formula are variables related to subscriber positions. That
is, the downlink capacity is related to the spatial distribution of subscribers,
and can be determined only through system emulation.
N
j
j
jjjDL vW
REbvsNoi
1
1
Downlink Interference Analysis
Simulation Result
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Simulation Result
Downlink Interference Analysis
Si l ti R lt A l i
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When the transmitting power of the base station is 43 dBm(20 W), a maximum of about 114 subscribers are supported.
In order to ensure system stability, the mean transmitting
power of the base station cannot exceed 80% of the
maximum transmitting power, namely, 42 dBm. Thus, about
112 subscribers are supported.
Simulation Result Analysis
How to Control the Interference
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How to Control the Interference
Impact of the interference to the network
Handover success rate
Access efficiency
Call drop rate
Voice quality
Method for interference control
Improve the precision of the power control
Improve the Rake receiving efficiency
Proper network planning
Contents
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Contents1. Traffic Model
2. Uplink capacity analysis
3. Downlink capacity analysis
4. Multi-service capacity estimation
5. Network estimation procedure
6. Capacity enhancement technologies
4. Multi-service Capacity Estimation
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p y
4.1 Facto rs th at restr ict the radio
network c apaci ty
4.2 Commonly used capacity design
method
4.3 Capacity estimation example based
on the Campbellstheorem
Factors that Restrict the Radio Network Capacity
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p y
The WCDMA network capacity is
restricted by the following factors in the
radio network part:
Uplink interference
Downlink power
Downlink channel code resources
Channel Element (CE)
Iub interface capacity
Downlink Transmit Power
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The downlink transmit power is divided into
two parts: One part is used for common
channels, and the other part for dedicated(traffic) channels.
The transmit power that is allocated by a
cell to subscribers varies with the service
demodulation threshold, propagation path
loss, and interference received by asubscriber.
The downlink transmit power of a cell is
shared by all the subscribers in the cell.
The emulation method is often used to
analyze downlink interference.
N
jCCHT PPP1
Downlink Channel Code Resources
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The WCDMA network uses the code
words with the SF ranging from 4 to
512. The smaller the SF is, the higherthe supported data rate is.
In a code tree, the allocable codes
must meet the following conditions:
No codes are allocated on the
path from this code to the rootnode.
No codes that take this code
as the root node are allocated
in this sub-tree.
Principle for code allocation:
Try to reserve the code words
with small SFs to improve the
utilization rate.
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
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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)
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The CE is the quantitative data that logically measures the resources occupied for
service processing.
The resources occupied for service processing are mainly related to the SF of the
service. The smaller the SF is, the greater the data traffic is, and the more the
occupied resources are.
The SFs of typical services are: AMR12.2 kbps SF = 128
CS64 kbps SF = 32
PS64 kbps SF = 32
PS144 kbps SF = 16
PS384 kbps SF = 8
Channel Element (CE)
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If we define the resources required for processing AMR services of 12.2 kbpsas a CE, the number of CEs occupied by other services are as follows:
AMR12.2 kbps 1
CS64 kbps 4
CS144 kbps 8
CS384 kbps 16
PS64 kbps 4
PS144 kbps 8
PS384 kbps 16
4. Multi-service Capacity Estimation
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5.1 Factors that restrict the radio
network capacity
5.2 Commonly u sed capaci ty design
method
5.3 Capacity estimation example based
on the Campbells theorem
Erlang-B Formula (I)
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Erlang-B formula is used for
estimating the peak traffic that meets
certain call loss rate when the average
traffic (Erlang) is given
Erlang-B formula is only used for
Circuit switched services
Single service
The WCDMA system provides CS
and PS domain multi-services
Erlang B Formula (I)
Erlang-B Formula (II)
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The prerequisite of the Erlang-B is that the requests for resources
should be based on Poisson distribution, that is, the resource request
variance should be equal to its mean value.
If, when a service establishes a connection, the service requests for
resources more than the unit resources, the resource request is no
longer equal to its mean value. The Erlang-B formula is not applicable in
this case.
Comparison of the methods for multi-service capacity estimation:
Post Erlang-B
Equivalent Erlangs
Campbells Theorem
g ( )
Post Erlang-B (I)
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g ( )
By summing up the capacities
required for different services,
we obtain the capacities
required for the combined
services.
The resource efficiency ofdifferent services is not taken
into account.
Post Erlang-B (II)
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Assume that two types of services share resources
Service 1: one unit resource/connection.12 Erlang
Service 2: three unit resources/connection.6 Erlang
Calculate capacity required for each service
Service 1: 12 Erlangs require 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
g ( )
Assume that two services use the same resources
Post Erlang-B (III)
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Post Erlang-B overestimates
the capacity requirements!
Assume that two services use the same resources.
Service 1: one unit resource/connection, 12 Erlangs
Service 2: one unit resource/connection, 6 Erlangs
Calculate the capacity required for each service.
Service 1: A 12-Erlang traffic volume requires 19 unit resources to meet the
blocking rate of 2%.
Service 2: A 6-Erlang traffic volume requires 12 unit resources to meet the blocking
rate of 2%.
In total, 31 unit resources are required.
However, the reasonable result should be as follows: A 18-Erlang traffic volume
requires 26 unit resources to meet the blocking rate of 2%.
Equivalent Erlangs (I)
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Combine different services
by converting the bandwidth
from one service to another,
and then calculate the
required capacity.
Selecting different services
as the measurement
benchmark may lead to
different capacity
requirements.
A th t t i h
Equivalent Erlangs (II)
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Assume that two services share resources.
Service 1: one unit resource/connection, 12 Erlangs
Service 2: three unit resources/connection, 6 Erlangs
If service 1 is taken as the measurement benchmark, the total traffic
volume for the two services is equivalent to 30 Erlangs.
A 30-Erlang traffic volume requires 39 unit resources to meet the
blocking rate of 2%.
If service 2 is taken as the measurement benchmark, the total traffic
volume for the two services is equivalent to 10 Erlangs.
A 10-Erlang traffic volume requires 17 unit resources (equivalent to
51 unit resources of service 1) to meet the blocking rate of 2%.
The forecast results
do not match!
Campbells Theorem (I)
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The Campbells Theorem sets up a kind of combined distribution.
Where:
indicates the service amplitude, that is, the channel resources
required for a single link of the service.
indicates the mean value, and v indicates the variance.
ia
cff icOfferedTra
c
aCCapacity ii
)(
ii
i
i
aErlangs
aErlangsv
c
2
A th t t t f i h
Campbells Theorem (II)
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Assume that two types of services share resources
Service 1: one unit resource/connection.12 Erlang
Service 2: three unit resources/connection.6 Erlang
The system mean value is
The system variance is
The capacity factor c is 1
6636112 222
iaErlangsv
3063121 iaErlangs
2.23066
vc
Campbells Theorem (III)
Combined traffic is calculated as follows:
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Combined traffic is calculated as follows:
A capacity of 21 resources is required to meet the blocking rate of 2%.
For the target services that meet a same Grade of Service (GoS), the
capacity required is as follows (based on the unit resource of service 1):
Target service 1: C1 = (2.2 x 21) + 1 = 47
Target service 2: C2 = (2.2 x 21) + 3 = 49
For a same GoS, different services require different capacities.For a given capacity, the GoSs of different services differ slightly.
63.132.2
30
cf f icOfferedTra
Comparison Between Common Capacity DesignMethods
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Post Erlang-B
Service 1 (one unit resource/connection, 12 Erlangs) and service 2(three unit resources/connection, 6 Erlangs) require 55 unit
resources in total.
Equivalent Erlangs
As calculated based on service 1 (one unit resource/connection, 12
Erlangs), a total of 39 unit resources are required.
As calculated based on service 2 (three unit resources/connection,
6 Erlangs), a total of 51 unit resources are required.
Campbells Theorem
Under the same conditions, 47 to 49 unit resources are required in
total.
Questions
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1. What are the factors that restrict the network
capacity?
2. What are the common methods for estimation of the
multi-service capacity?
Summary
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This chapter describes three methods for the estimation of the multi-
service capacity.
This chapter details the process of calculating the capacity with the
Campbells theorem.
Contents1. Traffic Model
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a c ode
2. Uplink capacity analysis
3. Downlink capacity analysis
4. Multi-service capacity estimation
5. Network estimation procedure
6. Capacity enhancement technologies
5 Network Estimation Procedure
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Capacity dimensioning ideas
Determining the Service Model
Determining the QoS
B d t ffi di t ib ti d hi l f t di id th
Capacity Dimensioning Ideas
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Based on traffic distribution and geographical features, divide the
areas to be planned into dense urban areas, ordinary urban areas,
suburban areas, and rural areas.
Perform traffic model analysis for all target areas.
Based on the traffic models in different target areas, determine
the single-TRX planning capacity of each target area.
Determine the NodeB quantity and TRX quantity in the target
areas that meet the capacity requirements.
Compare the NodeB quantity and TRX quantity determined
based on the capacity requirements with those based on the
coverage requirements. Select the larger NodeB quantity and
TRX quantity to meet the capacity and coverage requirements at
the same time.
Determining the Service Model
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Subscriber distribution data
Subscribers Distributed in Different Scenarios
(Number of Subscribers/km2)
Application
Scenario
2003 2004 2005
Dense Urban 11128 12060 18683Common Urban 462 499 676
Suburban 246 266 341
Rural 15 16 18
Road/Main Road 23 35 48
Determining the ServiceModel
P ti f b ib t diff t l l
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Proportion of subscribers at different levels
Proportion of Subscribers at Different Levels
(Consider This in Combination with the Time)
Application
Scenario
High-end Mid-end Low-end
Denseurban 40% 40% 20%
Common
urban
15% 25% 60%
Suburban 5% 25% 70%
Rural 1% 10% 89%Road/Main
road
1% 10% 89%
Determining the Service ModelCS Service Model
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ServiceType
PenetrationRate
BHCA AHT (s) ActivationFactor
Mean
Rate
(kbit/s)
AMR
speech100 % 1 90 0.5 8
Video
phone100 % 0.1 54 1 64
Determining the Service ModelPS ServiceModel of Low-end Subscribers
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Service
Type
Penetr
ation
Rate
BHSA
Packet Call
Number/Se
ssion
Packet
Number/Pa
cket Call
Packet
Size
(bytes)
Inter-Arrival
Time Between
Packet Calls(sec)
Email 10 % 0.10 2 32 480 320
Internet 30 % 0.18 5 25 480 412
Onlinegame, ICQ
25 % 0.10 2 3 480 8
Picture
and ring
downloadi
ng, FTP
25 % 0.10 2 62 480 5
Real-timevideo
0 % 0.00 1 267 1500 0
SMS 50 % 0.50 1 1 160 0
EMS /
MMS50 % 0.50 2 32 480 320
Determining the Service ModelPS Service
Model of Mid-end SubscribersInter-
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Service
Type
Penetrati
on Rate BHSA
Packet
Call
Number/S
ession
Packet
Number/Packet Call
Packet
Size(bytes)
Inter-
Arrival
Time
Between
Packet
Calls (sec)
Email 20 % 0.20 2 32 480 320
Internet 30 % 0.24 5 25 480 412
Online
game,ICQ
15 % 0.20 2 3 480 8
Picture
and ring
download
ing, FTP
15 % 0.20 2 62 480 5
Real-time
video 10 % 0.10 1 267 1500 0
SMS 100 % 0.80 1 1 160 0
EMS /
MMS100 % 0.80 2 32 480 320
Determining the Service ModelPS Service
Model of High-end SubscribersInter-
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Service
Type
Penetrati
on Rate BHSA
Packet
Call
Number/S
ession
Packet
Number/Packet Call
Packet
Size(bytes)
Inter-
Arrival
Time
Between
Packet
Calls (sec)
Email 30 % 0.30 2 32 480 320
Internet 20 % 0.30 5 25 480 412
Online
game,ICQ
5 % 0.30 2 3 480 8
Picture
and ring
download
ing, FTP
10 % 0.30 2 62 480 5
Real-time
video 20 % 0.20 1 267 1500 0
SMS 100 % 0.60 1 1 160 0
EMS /
MMS100 % 0.60 2 32 480 320
With the data service models of various user types we can obtain the
Determining the Service ModelPS Traffic ModelParameters
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With the data service models of various user types, we can obtain the
traffic model parameters for calculation.
Busy hour throughput per subscriber (kbit) = Penetration rate of low-
end subscribers x Busy hour throughput per low-end subscriber x
Proportion of low-end subscribers + Penetration rate of mid-range
subscribers x Busy hour throughput per mid-range subscriber x
Proportion of mid-range subscribers + Penetration rate of high-endsubscribers x Busy hour throughput per high-end subscriber x
Proportion of high-end subscribers
Theoretical length of a session (bytes) = Packet Call Num/Session x
Packet Num/Packet Call x Packet Size (bytes)
Average reading time (s) = (Packet Call Num/Session-1) x Inter-Arrival
Time Between Packet Calls (s)
Determining the QoS
Capacity dimensioning determines the capacity under certain QoS
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p y g p y
conditions. CS services generally take the call loss or blocking rate toevaluate the GoS. PS services generally take the acceptable time delay or
acceptable minimum throughput to evaluate the GoS. In the tender
documents of operators, the GoS of PS services is sometimes described
as call loss.
Determining the QoS
Requirements for the blocking rate
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q g
BlockingRate
VoiceService
SMS
MMS
VideoStream
AudioStream
VideoPhone
VideoConference
Internet Email
Dense
urban
Common
urban
Suburban
Rural
Road/mai
n road
Contents1. Traffic Model
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2. Uplink capacity analysis
3. Downlink capacity analysis
4. Multi-service capacity estimation
5. Network estimation procedure
6. Capacity enhancement technologies
Transmit Diversity
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The transmit diversity can increase the downlink capacity
and coverage.
Conclusion on capacity improvement through the transmit
diversity
STTD mode: capacity increase by 17% to 24%
TxAA(1) mode: capacity increase by 16% to 23%
TxAA(2) mode: capacity increase by 31% to 37%
Sectorization
In dense urban areas and ordinary urban areas with high traffic,
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y g ,
increasing the sectors in a NodeB helps increase the capacity.
A six-sector NodeB generally uses the antennas with the horizontal lobe
33.
The capacity of a 6-sector NodeB is 1.67 times as large as that of a three-
sector NodeB.
Capacity enhancement technologies of GSM
Division of cells
Closer frequency reuse
Adding micro-cellular devices
Extended frequency bands
CoBCCH
Half rate
Comparison Between GSM Capacity Expansion
and WCDMA Capacity Expansion
GSM capacity expansion
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GSM capacity expansion
If capacity expansion is required for the network in future, you only need toallocate new channels to the corresponding cells. Other changes are not needed
on the network as long as the expansion does not exceed the maximum capacity
(determined by the frequency resources and frequency reuse) of cells. Otherwise,
new NodeBs or sectors must be added with frequency planning.
WCDMA capacity expansion
The handover between different frequencies (or between the TRXs in a samesector) in the WCDMA system is hard handover, which requires you to enable the
compression mode and occupies huge system resources. Therefore, the capacity
expansion of the WCDMA system is not as simple as that of the GSM system,
which can be implemented through additional TRXs to the corresponding cells.
The method of dividing cells for capacity expansion also costs a lot. In the early
phase of capacity dimensioning, a signal margin must be determined to serve as
a compensation for interference in the case of traffic increase during thecalculation of the cell area.
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Thank You!
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