Performance Study on the Effects of Cell-Breathing in WCDMA

7/28/2019 Performance Study on the Effects of Cell-Breathing in WCDMA

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Performance Study on the Effects of Cell-Breathing in WCDMA

Kay Leong Thng1,2, Boon Sain Yeo1 and Yong Huat Chew1

1Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 1196132Electrical and Computer Engineering Dept., National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260

Abstract The performance of CDMA cellular systems over the

air is generally interference-limited. A phenomena arising from

this is the effect of cell-breathing in 3G cellular systems. Cell-

breathing is the expansion or contraction of the effective coverage

of a cell in response to the number of active mobiles (MSs) in a

network. If it is not well controlled, communication failure may

result. With 3G offering different classes of services, planning and

dimensioning of such high-quality radio networks thus requires

extensive planning tools. Despite the progressive rolling out of 3G

systems worldwide, the effects of cell-breathing are not well

understood. In this work, a simulator is set up to study the effect

of cell breathing. Simulation results show how the area where

reliable coverage can be provided by a base station (BS) may

varies as the density of MSs changes within it. Current work

emphasizes on the downlink simulation with a single BS followedby a similar simulation involving 2 BS. The results demonstrate

how cell-breathing may cause communications failure.

I. INTRODUCTIONUniversal Mobile Telecommunication System (UMTS) is a

third generation mobile network designed for multimedia

communication which enables person-to-person communi-

cation with high quality images and video. With UMTS, access

to information and service will be enhanced by higher data

rates, and Wideband CDMA (WCDMA) is to be employed

over the air interface.

With 3G mobile network based on WCDMA, users (or

MSs) can occupy the entire allocated frequency and timedomain, and are distinguished from each other through the use

of unique codes. The codes for other MSs will appear as noise,

in which either codes are non-orthogonal or there is loss of

orthogonality due to the presence of multipath. As the number

of MSs increases, the level of interference rises. From [1], to

successfully demodulate the transmitted user data bits, the SIR

must be greater than a threshold. Failure to do so will result in

momentarily poor link quality and even the call will be

dropped. The greater the number of users in a cell, the greater

the interference, hence, the larger will be the required received

power. If we consider that a MS and a BS have a fixed

maximum transmit power, changing the required received

power will have the same effect as changing the maximum path

loss that can be tolerated between the BS and the MS. In other

words, the effective area where reliable quality of service

(QoS) can be provided by a BS will decrease. This is known as

cell-breathing. Thus, the fundamental drawback of cell-

breathing is that the desired SIR of a MS may not be achievable

at some times of the day even though he may be at the same

location, and that experience will be truly annoying.

A number of researches have been carried out to reduce this

impact of cell breathing [2-5]. In [6], it is shown that cell-

breathing may even give an advantage with the possibility of

improving the capacity in some cells with higher traffic by

redirecting MSs to surrounding cells. However, this

deployment is not quite realistic, and cell-breathing does not

provide much advantage from a system capacity point of view

if the traffic demands in all the cells are high. As shown in [7],

there is a trade-off between capacity and coverage. It is well

known that the coverage of a cell has an inverse relationship

with the density of MS in a cell. However, the exact

relationship between capacity and coverage is still very much

indistinct presently.

With the rapid development of 3G mobile communication

system and the possibility that many countries will be adopting

this system in the next few years, it is crucial that tools aremade available for network planners. Simulators are often the

tools being used. In this paper, a detailed framework of a 3G

system simulator, based on 3GPP, will be developed. With the

development of this simulator, various characteristics of a 3G

system can then be studied intensively (e.g. cell breathing, bit-

error-rate and call blocking). Besides, work on the optimization

of the system or network planning can also be carried out with

the aid of this tool.

This paper continues in Section II with an overview on the

structure of the simulator which has been developed for our

study of cell-breathing. Section III describes the simulation

scenario with one cell and the corresponding result obtained is

presented and discussed. Section IV extends the simulation totwo cells. Finally, some conclusions are given in Section V.

II. SIMULATOROVERVIEWAccuracy of a simulator depends very much on the choices

made for the physical layer model. By and large, a compromise

has to be made between (i) accuracy and (ii) complexity when

developing such simulator. Based on the work from [10], there

are basically 2 classes of simulator Static &Dynamic. Static

Simulatorare based on Monte Carlo approaches and essentially

work by dropping MSs in a defined network layout, and

thereafter using some algorithm to decide which proportion of

the MSs can be correctly served. Then the process is repeated

with a number of drops, where in each case the MS spatialdistribution and numbers correspond to a realization of a global

statistical model of network load. The performance indicators

will eventually converge, after which the process can be re-run

for another set of parameters.

On the other hand, a Dynamic Simulator works on the

principle of two time scales namely short-term and long-term.

If interest is on admission control, overload control, handover

and call dropping, then there is no requirement to consider

0-7803-9206-X/05/$20.002005 IEEE


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micro time scales, and are referred to as Long-Term Dynamic

Simulator. If interest is focused on functions such as packet

scheduling, fast cell selection, random access channel or

forward access channel performance, then the models should

include short timescales and this kind of modeling is referred to

as Short-Term Dynamic Simulator.

In that paper, it has been justified that a dynamic simulator

has an edge over static simulator in terms of accuracy. It is

because a static simulator has no concept of time unlike adynamic simulator which is able to take into consideration

characteristics of the system like MSs mobility, admission

control, handover, call generation and generally time-

dependent algorithms.

A short-term dynamic simulator also takes into account the

detailed model of the effects such as channel fading, inner loop

power control, packet scheduling and fast cell selection etc.

However, these characteristics are not considered in a long-

term simulator. Thus, it can be concluded that short-term

dynamic simulator offers the greatest flexibility and potentially

a better accuracy than the long-term one, albeit at the expense

of extra-complexity at the system level.

With a long term goal in mind, our aim is to develop asimulator with the concepts of a short-term dynamic simulator.

The characteristic of a short-term dynamic simulator can be

summarized as follows:

Taking the time slot duration as the time step of the system.

For example, if the inner power control loop has time

duration of 0.666ms, we shall have the simulator to work

on time steps of 0.666ms, i.e., the calculation of SIR values

are done and updated every 0.666ms.

This example provided us with an intuitive approach to

model our simulator algorithm. In general, the design of this

simulator is structural, with a top-down approach. The exact

algorithm of our simulator will be discussed more in detailsover in this section.

The simulator will be built upon 2 main modules

Environmental Module and Protocol Module. Contained in

these 2 modules will be sub-models, which can be considered

as C-functions that the main program will call when the

simulation is run.

IIA Environmental Module

The Environmental Module serves as an interface between

the user of the simulator and the simulator itself. In will contain

an interface for the reading of input parameters or data and

another interface where the results of simulation are shown.

The structure of this module is shown in Fig. 1.

Fig. 1 Environmental module

Parameter Structure and Radio Channel

This consists of 3 data structures namely MobileStation,

BaseStation and System. MobileStation and

BaseStation carry variable parameters of all MS and BS

respectively, while System carries the variable parameters for

radio channel behaviours such as the 3G operating parameters.

Systems will also carry the simulation results. The appendix

shows the parameters to be carried by System.

Simulate 3G network

This contains the whole of the simulation program and is part

of the Protocol Module. Most of the parameters used in this

module is based on release 99.

Results

This provides the interface which presents the results to the MS.

II.B Protocol Module

The Protocol Module contains the heart of the simulation

program. It consists of 4 main functions namely Generate,

Propagate, Receive and Control. These functions in turn

contain numerous sub-functions. Fig. 2 shows the Behavioural

Module.

Fig. 2 Behavioural module

In general, the sequence of the program shall be top-down

starting (after Initialize) from Generate to Propagate,

Receive and finally Control. Each main function will be

started with a Trigger (a call) from the main controller. Upon

completion of its task, it will response to the main controller

with a status of Ready (a return). Similarly, the sub-functions

within each main function will be started and completed in the

same way. Fig. 3 shows the highest level data flow diagram of

the protocol module and shall explain the design clearer. It

shows the flow of data after Initialize. A description of the

functions and its sub-function, in the sequence of the flow of

the simulation are as follows:

Fig. 3 Data flow diagram of the overall protocol module

1.

Initialize

2.

Generate

3.Propagate

4.

Receive

5.

Control

6.Control

Simulation

ResultsParameter Structure

ParameterStructure

RadioChannel

T

T

TT

T

R

RR

R

R

Data flow

Signal flow

Simulate

3G

network

Generate

PropagateReceive

Control

Parameter

Radio

Simulate

3G

networkResults


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Initialize

This module marks the start of the simulation program.

Characteristics of all MS and BS are defined. The attributes of

each MS (location, speed of motion, direction of motion,

service class and duration of call) are generated randomly. The

required connection and transmission power for each entity are

also established with minimum transmission power.

GenerateIn this state, existing calls will first be checked if it is to be

ended normally i.e. the call duration has expired. New calls and

its duration will be then generated according to a certain

statistical distribution.

3GPP has stated 4 service classes namely - conversational

real time, interactive, streaming, background. We can represent

each of these classes with a fixed data rate. The data rate

corresponding to each class of service and an example of its

application are shown in Table 1.

TABLE 1

QoS classes Data

speed

Error-tolerant Error-Intolerant

Conversationalreal time

12.2kbps Voice, video Interactive games,Telnet

Interactive 64kbps Voice messaging Web browsing, ATM

Background 144kbps Fax Email

Streaming 384kbps Audio, video FTP data transfer

Table 1. Data rate corresponding to each class of service.

From [2, 3, 11] the call arrival distribution, duration, and

average speed of each MS for each class of services are

tabulated in Table 2. Probability of generating each new call is

based on a Poisson distribution; duration is based on a

exponential distribution; speed is based on Gaussian

distribution.

TABLE 2

Data speed Arrival rate/min Duration Avg Speed

12.2kbps 0.03333 120s 50km/h64kbps 0.01667 288s 40km/h

144kbps 0.00833 192s 30km/h

384kbps 0.00833 300s 20km/h

Table 2. Statistical call distributions, call duration.

Propagate

This state consists of 3 sub-functions namely Mobility,

Signal Propagation and Received Power Calculation.

Mobility

In this sub-function, the new position of each MS will be

calculated according to its old position, velocity, simulation

time step and predefined route.

Signal Propagation

In general, this sub-function will determine the drop in

transmitted power along the channel (both uplink and downlink)

given the transmission power. In this sub-function, the

propagation model is based on COST-Hata-Model. The loss in

dB is given by:

( )

( ) mb

mb

CRh

hahfL

++

+=

loglog55.69.44

log82.13log9.333.46(1)

where f is 2150MHz in downlink and 1950MHz in uplink,

bh = 8m is the effective height of the BS, mh =1m is the height

of MS antenna, R is the distance between BS and MS in km,

( ) ( ) ( )8.0log56.17.0log1.1 = fhfha mm and mC is 0dB formedium-sized city and suburban centers.

Received Power CalculationIn this sub-module, the power at the receiving end is

determined after propagating through the channel. The result of

its computation will be used by sub-function Interference for

the determination of SIR.

Receive

This state consists of 2 sub-functions namely Interference

Calculation and SIR Calculation. With the determination of

interferences in the uplink as well as downlink, the SIR in the

uplink and downlink can then be calculated. From [5] and[6], a

detailed approach to determine the interference and SIR has

been established according to the following equations.

Uplink SIR

( )k

K

kjj

ijljjj

L

illklkklk

R

WxdSvNWxdS

1

1,,

10,,,

==

+=

(2)Downlink SIR

( ) ( ) klklL

lii

K

j

ijijjj

K

j

ljljjjjk

lkklk

k

R

W

dTxdSvxdSvNW

xSd

,

1 1

,,

1

,,0

,,

1

+++

=

= ==

(3)

In (2) and (3),L is the total number of cells and Kis the total

number of MSs in the area under consideration. The notation

ijx , is used as an assignment variable so that 1, =ijx if MSj is

connected to BS i, or else 0, =ijx . The index l is used to

represent the reference cells where the kth MS is attached to,

i.e., 1, =lkx and the assignment of BS is based on largest

received power.Rk is the data rate ofk(bps), Wis the chip rate

(Hz) and the value used is 3.84Mcps, Sk is transmission power

(dBm) assigned to kth MS,k is the desired received 0/NEb of

kth MS (downlink) or BS (uplink) and dk,l is the attenuation

between BS land MS k (dB). kis the orthogonality factor inthe downlink ofkranged between [0..1] and a value of 0.75 is

used for those MSs in cell l in our simulation. Similarly vk is

the activity factor of and ranged between [0..1] and the value

used is 1.0. Tlis the transmission power at BS lused by all the

common channels and the value used is 36.0dBm. There are

power controls in both the uplink and downlink associates with

maximum allowable transmit power.(N0)l is the thermal noise

power density at lgiven by -169dBm/Hz. For any parametery


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given in decibel, its linear value isy . Although it is not clearly

distinguish, some of these parameters such as activity factor,

path loss, etc., should be different in the uplink and downlink in

practical scenarios. However, in our simulation, we assume that

these parameters are the same.

Control

This state consists of 4 sub-modules namely Admission,Quality, Handover and Power Control.

Admission

This sub-function will decide upon whether to let a new call

into the system after successful synchronization. Call

admission algorithm is not specified by 3GPP and may varywith different operators. In our simulator, the call admissioncontrol implemented is based on Receive Power CallAdmission Control (RPCAC). In general, a new call is allowed

if the total received power at the BS, l, is below a threshold,Ith.

When a new MS requests a call, the algorithm will estimate the

increase inI, I, that the MS will cause to the system. The callwill be admitted if the overall system load is still below the

threshold value, otherwise it will be rejected.

I + I


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from the BS. This sharp drop in radius shows that the system

requires a significant number of MS, active at the same time, to

trigger off cell-breathing which in this case is a great drop inarea where reliable signals can be provided. The resulting

radius where reliable signals can be provided by the BS is then

approximately 2775m, which corresponds to a 10% decrease in

radius.

Fig. 4 Coverage plot for 1 cell.

Table 5 contains a summary of the total number of callsgenerated, blocked or successful.

TABLE 5

Call Type

12.2kbps 64kbps 144kbps 384kbps

Generated 1851 989 543 576

Dropped 1311 918 524 563

% Dropped calls. 70.9% 92.8% 96.5% 97.7%

Success 307 35 16 5

% Successful calls. 16.6% 3.54% 2.94% 0.87%

Table 5. Summary of simulation results.

The low percentage of successful calls by MS stems from

the fact that there is only 1 BS supporting such a large numberof MS. One would not expect to see this in practice because wepurposely overload the system to observe the cell breathing

impact. In practice, proper cell planning would require more

BS to support the number of MS and service requests.

IV. SIMULATION WITH TWO CELLSScenario

In this simulation, 2 cells are located across the simulation

area, which is a 100 100 map square. BS A is located at

coordinate point (35, 35) while BS B is located at (65, 65). MSand its different classes of service are distributed close to BS A

at the start of simulation. Upon the system reaching stability,

all MS will move towards BS B. Cell-breathing can then be

observed and analyzed. In this simulation, the coverage area

recorded is specific on the downlink for call-type of 12.2kbps.Table 6 below shows a summary of the simulation

conditions.

TABLE 6

Parameters Values

No of BS 2

No of MS 500

Simulation time step 10ms per step

Map scale 120m per division

No of simulation steps to stability 12000 steps

Convergence rate of MS 12m/s per step

Table 6. Summary of simulation conditions.

Results and discussion

Fig. 5 shows the combined graphical plots for BS A and BS

B with respect to time. With the concentration of MS shiftingfrom BS A to BS B, the area where reliable coverage can be

provided by BS A and B are seen to be reciprocal of one

another, which coincides with theoretical analysis for the

characteristics of cell-breathing. The magnitude of expansion

or contraction of the 2 BS coverage areas is in the same degree

as with the case for 1 cell. This result provides an insight thatcell-breathing is not infinite.

Cell-breathing is in general accounted by the spatial

distribution of MSs. The cap to the maximum transmission

power allowed for a transmitter is also another factor. Thislimit on transmission power causes MS that are not receiving

the target SIR to be dropped early and thus, interference withinthe system are reduced before it gets too large to trigger off

further cell-breathing.

An important observation from this simulation is the

possibility of uncovered area resulting from cell-breathing.

This period of unreliability is observed when the MS starts

moving towards BS B (between simulation time step 25000 to38000 as shown in Fig. 6). It is during this period of time that

the shrinkage of reliable coverage by BS B may not be well

compensated with a corresponding expansion of reliablecoverage by BS A. Thus, giving rise to potential

communication failure.

Fig. 5 Coverage plot for BS A and BS B.


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TABLE 7

Call Type

12.2kbps 64kbps 144kbps 384kbps

Generated 931 544 322 328

Dropped 697 482 306 317

% Dropped calls. 74.9% 88.6% 95.0% 96.6%

Success 155 36 14 11

% Successful calls. 16.6% 6.62% 4.35% 3.35%

Table 7. Summary of simulation results.

V. CONCLUSIONSIn this paper, the structure and features of a 3G simulator

were proposed and discussed. Simulations were then carried

out to study the effect of cell-breathing via the variations of MSdensity, firstly with a single cell, followed by 2 cells.

The result obtained shows that with the increase in density

of MS, the coverage area from a cell will generally decrease.

This is consistence with theoretical characterization of cell-

breathing. A more concerned effect of cell-breathing is the

possibility of communication failure as a result. Thus, toprovide a good and reliable communication system, network

planning needs to be meticulous in ensuring a proper control onthe extent of cell-breathing.

Another interesting result is that cell-breathing is finite. In

other words, one can neither expect the coverage of a cell thatis subjected to a high density of MS to decrease to zero nor can

the coverage of a cell with a low density of MS be extremely

large. The result presented is important in providing an insight

into how coverage area, on the downlink, is also being affected

with a restrain on transmission power. Further studies are to

look into how to jointly consider cell breathing effect in cellplanning, especially in heavy load conditions, so that the

network can maintain at a reasonably low dropped call

probability.

REFERENCES

[1] P R Gould, Radio Planning of Third Generation Networks in Urban

Areas, 3rd International Conference on 3G Mobile CommunicationTechnologies, 2002, Conf. Publ. No. 489, 8-10 May 2002, pp. 64 68.

[2] S.T. Yang and A. Ephremides, Resolving the CDMA Cell Breathing

Effect and Near-Far Unfair Access Problem by Bandwidth-SpacePartitioning, 53th IEEE Vehicular Technology Conference, vol. 2, 6-9

May 2001, pp. 1037 1041.

[3] A.I. Zreikat and Al Begain K., Soft Handover-based CAC in UMTSSystem, 10th International Conference on Telecommunications, vol.

2, 23 Feb-1 March 2003, pp. 1307 1312.

[4] J. Yang and J.S. Lin, Optimization of Power Management in a CDMAradio Network, 52nd IEEE Vehicular Technology Conference, vol.

6, 24-28 Sept. 2000, pp. 2642 2647.

[5] M.MAl. Akaidi and H. Ali, Performance Analysis of Antenna

Sectorisation in Cell Breathing, 4th International Conference on 3G

Mobile Communication Technologies, Conf. Publ. No. 494, 25-27 June

2003, pp.98 103.[6] A. Jalali, On Cell Breathing in CDMA Networks, IEEE International

Conference on Communications, vol. 2, 7-11 June 1998, pp. 985 988.

[7] V.V. Veeravalli and A. Sendonaris, The Coverage-Capacity Tradeoff inCellular CDMA Systems, IEEE Transactions on Vehicular

Technology, vol. 48, no. 5, Sept. 1999, pp. 1443 1450.

[8] J. Buczynski, P. Gajewski and J. Krygier, Modelling of the ThirdGeneration Mobile System,IEEE AFRICON, 1999 vol. 1, 28 Sept.-1 Oct.

1999, pp. 251 256.

[9] F. Babich, L. Deotto, A Formal Approach to Modeling and Performance

Analysis of Shared Channels for Real-Time Services in W-CDMA 3GSystems, 52th IEEE Vehicular Technology Conference, vol. 4, 24-28

Sept. 2000, pp. 1639 1645.

[10] E. Villier, L. Lopes, S. Lambotharan, Approaches to Modelling the

Physical Layer Performance in a UMTS Radio System Simulator, 3rd

International Conference on 3G Mobile Communication Technologies,

Conf. Publ. No. 489, 8-10 May 2002, pp. 560 564.[11] S.T. Yang and A. Ephremides, Resolving the CDMA Cell Breathing

Effect and Near-Far Unfair Access Problem by Bandwidth-space

Partitioning, 53rd IEEE Vehicular Technology Conference, vol. 2, 6-9May 2001, pp. 1037 1041.

Appendix

List of parameters and its values used by SystemsSymbol Description Data

type/Remarks

chip_rate Chip rate of system 3.84MCPS

step_time Each simulation steptime

10ms

N_THERMAL_NOISE Thermal noise power

density

-169dBm.

ORTHO_FACTOR Orthorgonal factor 0.75

Ith Total received power at

BS for call admission.

100dB

COMMON_CH_PW Common ChannelPower 30dBm

HO_DL_THRESHOLD Handover DL threshold 0.5dB

HO_UL_THRESHOLD Handover UL threshold 0.5dB

UP_POWER_STEPSIZE Up powercontrol

stepsize

1dB

DOWN_POWER_STEPSIZE

Down powercontrolstepsize

1dB

FREQ_DL Frequency of DL

transmission

2150MHz

FREQ_UL Frequency of ULtransmission

1950MHz

EFF_HT_BS Effective height of BS. 30m

EFF_HT_MS Effective height of MS. 2m

MS_TX_MAX Max transmission powerof MS.

+30dBm

MS_TX_MIN Min transmission powerof MS. +5dBm

BS_TX_MAX Max total transmissionpower of BS.

+43dBm

BS_ TX_MIN Min total transmission

power of BS.

+5dBm.

BS_max_power_service

Max power per servicefor 12.2kbps, 64kbps,

144kbps and 384kbps.

+30dBm,+36dBm,

+36dBm, +38dBm

respectively

SIR TARGET FOR

VARIOUS SERVICES

12.2kbps Uplink / Downlink +6.5dB / +4.5dB

64kbps Uplink / Downlink +4.0dB / +4.0dB



Performance Study on the Effects of Cell-Breathing in WCDMA

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