resource allocation schemes high speed wireless access ... · the scheduling schemes exploit the...

Resource Allocation Schemes for High Speed Wireless Access Networks

Rathneswaran Vannit hamby

-1 t hesis submit ted in conformi ty wi t h the requirements

for the Degree of Doctor of Philosophy,

Department of Electrical and Cornputer Engineering:

at the University of Toronto

@ Copyright by Rathnemaran Vannithamby 2001

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To my late father and rny family

Resource Allocation Schemes for High Speed Wireless Aeeess Networks

Rat hneswaran Vannit hamby Degree of Doctor of Philosophy

Department of Electrical and Cornputer Engineering University of Toronto

2001

Abstract

The focus of this thesis is to efficiently allocate the radio resources in novel ways to achieve

performance benefits in CDMA systems through proper channel access control techniques.

The capacity of the integrated services with different data rates and quality of service re-

quirements is analyzed. It is first shown that the system performance deteriorates when voice

and high data rate services are integrated. To improve the system performance. a channel access

control technique for deiay insensitive high data rate traffic is proposed.

Resoiirce allocation and schediiling sciiemes arc! proposed and st iidietl for a systeni wi t h only

high data rate Internet users. The scheduling schemes exploit the packet mode trarisniission

to achieve better performance in the time-slotted systems where rates are aliocated according

to the channel conditions. These schernes are compared in terms of throughput and delay. and

also fairness in the allocated data rates. It is shown that the overall system performance is

severely afTected by the adverse channel conditions seen by a few users. To improve the system

performance. a technique is proposed that identifies and delays the transmission for stich users

until the conditions improve.

A tirne muse duster size of three c e b is considered tu mininrize the interference. Whcn

the cluster size is Iarger than one cell, the transmission From neighboring base stations niust be

coordinated. To efficient Iy reuse the t ime, aigorithms are developed for best-effort and real-t ime

services. A dynamic time share allocation algorithm is proposed and studied that can uniquely

coordinate the transmission time for high data rate Internet users in time reuse clusters larger

than one ceI1. A time slot assignment scheme is &O proposed and studied for real-tirne high data

rate services. It efficiently packs and dynamicdy ailocates the time slots so that the cal1 blocking

probability is lower than various fixed and dynamic schemes in aU t r a c conditions.

Acknowledgements

1 would like to thank my supervisor Prof. Elvino S. Sousa for his invaluable advice.

guidance. support and patience. He has always been accessible and very helpful.

1 would also like to thank my Ph.D. Committee. Prof. R. Bonert. Prof. J. Friedlander

(Mat hemat ics Dept .) . Dr. B. Hashem (extemal appraiser from Norte1 Networks) . Prof. K.

Y. Plataniotis and Prof. A. 3'. Cénetsanopoulos.

1 am thankful to my colleauges in communications group, especially the wireless group

for providing a pleasant working atmosphere and good conversation. Special thanks t o

Alagan, Halim and Wilson.

Sarah Cherian's and Diane Bettencourt Silva's administrative help are also grateful1y

acknowledged.

During this work. 1 IVBS assisted by the financial support provided by NSERC. CITO.

OGS, and the Lniversity of Toronto Open Fellowship.

1 am deeply grateful to my wonderful family for their never-ending care.

Contents

Abstract ii

Acknowledgement s iii

Contents iv

List of Abbreviations and Acronyms viii

List of Mat hematical Terrns x

List of Figures xii

List of Tables xv

Int roductioa 1

1.1 Overviem of CDMA Cellular Systems . . . . . . . . . . . . - . . . . . . . . 1

1.1.1 MultipleAccess. . . . . . . . . . . . . . . . . . . . . . . . . . . - . 3

1.1.2 Mobile Radio Propagation . . . . . . . . . . . . . . . . . . . . . . . 4

1.1.3 Power Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1 1 Spatial Reuse . . . . . . . . . . . . . . . . . . . . . . . . . . - . . . - 3

1.1.5 Wideband CDMA Systems . . . . . . . . . . . . . . . . . . . . . . . 6 - 1 -2 Radio Resource .~llocat ion . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.3 Motivation and Thesis Outiine . . . . . . . . . . . . . . . . . . . . . . . . . 9

- 4 Thesis Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2 Channel Access Control Schemes for Integrated Services 16

2.1 fntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2 System hlodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.1 Cellular System Mode1 . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.1.2 Traffic Mode1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2.3 Interference Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 VSG-CDLIA System Capacity . . . . . . . . . . . . . . . . . . . . . . . . . '20

2.3.1 Interference Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.3.2 Bit Error Probability Analysis . . . . . . . . . . . . . . . . . . . . . 24

2.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

. . . . . . . . . . . . . 2.4.1 Mean and Variance of Inter-ce11 Interference 28

2.4.2 Capacity of the Systeni with Voice and Data Csers . . . . . . . . . 29

2.4.3 Capacity of the System with Low Data Rate and High Data Rate

2.3 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3 Rate Allocation and Scheduling Schemes 39

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2 System Mode1 and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.2.1 CellularSystem'1Iodel . . . . . . . . . . . . . . . . . . . . . . . . . 42

3 - 2 2 Time-Slot ted Structure and Transmission Modes . . . . . . . . . . . 4.3

3.2.3 Traffic Mode1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.2.4 Rate and Powr .i llocations . . . . . . . . . . . . . . . . . . . . . . 45

3.3 Scheduling Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.3.1 Scheduling Schemes for TDM Mode Transmission . . . . . . . . . . 49

3.3.2 Scheduling Schemes for CDM Mode Transmission . . . . . . . . . . 52

3.3.3 Impiement ation of Rate and Ponrer Allocations . . . . . . . . . . . . 36

-- 3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a 4

.. 3.4.1 Effect of Shadowing . . . . . . . . . . . . . . . . . . . . . . . . . . . a t

3.4.2 Transmission Backoff Probabiiity and Performance Trade-off . . . . 59

3.4.3 Performance Cornparison of Scheduling Schemes . . . . . . . . . . . 69

3.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4 Transmission Time Coordination Among Base Stations 67

-4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.2 Systern Mode1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.3 Dynamic Time Coordination: Comple'aty and Effectiveness . . . . . . . . Cl

1.3.1 Complexity of Dynamic Schemes . . . . . . . . . . . . . . . . . . . 7 2

-.- 4.3.2 Effectiveness of Various Schemes . . . . . . . . . . . . . . . . . . . . ta

LI 4.4 Time Coordination Schcmes . . . . . . . . . . . . . . . . . . . . . . . . . . r r

-- 4.4.1 Fixed Time Share .-\llocation (FTSA) . . . . . . . . . . . . . . . . . r r

4.4.2 Dynamic Time share .-\ llocation (DTSA) . . . . . . . . . . . . . . . 79

4.5 SimulationResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8;

4.51 Time Reuse Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.3.3 Range of Time Share .4 llocations . . . . . . . . . . . . . . . . . . . 87

-1.5.3 Algori t hni Execution Time . . . . . . . . . . . . . . . . . . . . . . . 85

4 . 5.4 Enhancenients to the DTSA-AIg . . . . . . . . . . . . . . . . . . . . 89

4.6 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.7 Appendix A: A Numerical Example . . . . . . . . . . . . . . . . . . . . . . 90

5 Time Slot Allocation Schemes 93

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.2 System Mode1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4

3 Channel Allocation Schernes . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.4 Unified Channel -1llocat ion (UCA) Scheme . . . . . . . . . . . . . . . . . . 97

5.41 Virtual Channel Set (VCS) . . . . . . . . . . . . . . . . . . . . . . . 98

5.5 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5 1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 100

5 - 5 2 lmplernentation of Channel Assignment Scliemes . . . . . . . . . . . I O 1

5.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5 - 5 4 Irnplenientation Issues . . . . . . . . . . . . . . . . . . . . . . . . . 107

5.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

6 Conciusions 110

6.1 Thesis Surnrnary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

6.2 FutureCVork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Il4

Bibliography 116

List of Abbreviations and Acronyms

AMPS

AR$

BER

CDF

CDM

CDMA

DCA

DTSA

DTSA-Alg

DS

ER

EP

FDD

FEC

FF

FCA

FTSA

FTSA-A1g

FDMA

IP

MAI

MC-CDMA

MTSO

PN

QoS

RR

advanced mobile phone system

automatic repeat request

bit error rate

cumulative distribution function

code division multiplexing

code division multiple access

dy namic channel allocation

dynamic time share allocation

dynamic time share allocation-aigorithm

direct sequence

equal rate

eqiial power

frequency division duplex

fonvard error correct ion

fastest first

fked channel allocat ion

fixed time share allocation

h e d time share allocat ion-aigorit hm

irequency division multiple access

internet protocoi

multiple access interference

mu1 t i code-code division multiple access

mobile t elephone swi t ching office

pseudo noise

quality of senrice

round robin

SIR

TD-CDMA

TDD

TDM

TDMA

UCA

VCS

VSG-CDMA

WCDMA

signal to interference ratio

time division-code division multiple access

t ime division duplex

time division multiple'ring

tirne division multiple access

unified channel allocation

virtual channel set

variable spreading gain-code division multiple access

wideband code division mu1 t iple access

List of Mathematical Terms

B number of base stations

B, j t h base station

Cj ce11 that h a the base station Bj

G spr~ad-spwtrum processing gain

I interference (sum of intra-ce11 and inter-ce11 interference)

Ic inter-ce11 interference of class-c users

hi number of active mobile terminals in a ce11

i ith mobile terminal

Mms.4 maximum number of users that can be accepted in a time reuse ciuster iising DTSA

-\[FTS.4 maximum number of users that can be accepted in a ceil using FTSA

number of class-c users in a sector

number of da ta users in a sector

number of high data rate users in a sector

number of low data rate users in a sector

number of voice users in a sector

receive power level

transmit power level

total transmit power from base station B,

data rate for mobile terminal ,\Ir

data rate for mobile terminal JI, in time segment Ts in the EP scheme

rate of data users

rate of high data rate users

rate of low da ta rate users

rate of voice users

t ime duration of a frame

t ime share allocation for mobile terminal Mi

Tmin guaranteed minimum time share for a user

Ts time segment s in EP scheme

IV spread spectrum bandwidt h

Eb/Io signal bit energy to interference ratio

E() espected value

Var ( ) variance

BER requirement for class-c users

fraction of tratismit power allocated for mobile terminai ,II, in a ce11

channel gain From mobile terminal .\fi to base station B,

distance-potver-law coefficient

activity variable For class-c users

activity level for class-c users. where P(lltc = 1) = a,

activity level of data users

activity level of high data rate users

activity level of low data rate users

activity level of voice users

density of class-c users

standard deviation of the log-normal random variable that characterizes shadowing

background noise power

power spectral density of background noise

fraction of power allocated for the user data in downlinks. i.e.. (1 - . 3 ) for pilot

Eb/ lo requirernent

List of Figures

1.1 Frequency and time reuse clusters. . . . . . . . . . . . . . . . . . . . . . .

1.2 Interference for mobile terminal L l l j in (a) uplink and (b) downlink. . . . .

1.3 Broad band wireless access network. . . . . . . . . . . . . . . . . . . . . . .

2.1 The mobile terminal AIi is communicating with the base station Bq and

causes interference at the reference base station B,. . . . . . . . . . . . . .

2.2 Cellular network with 23 square cells. the reference ce11 is in the center. . .

2.3 Admissible region of the 5 MHz CDbI.1 channel with voice and data users

for various Rd/&. R, = 8 Kbps, a, = 318. ad = 1. 7, = -fd = 7 dB. . . . .

2.4 .ldrnissible region of the 5 MHz CD'VlA channel with wrious data activity

levels (ad) Rd/& = 4, R,, = 8 Kbps. a, = 318. Y, = -td = 7 dB. . . . . . .

2.5 Capacity of the 5 MHz CDM.1 channel with various Eb/Io requirements for

data ( ~ 1 ~ ) . Rd/& = 4, R, = 8 Kbps, a, = 3/8, ad = 1, 7, = 7 dB. . . . . .

2.6 Admissible region of the 5 MHz CD411 channel with low data rate and high

data rate users wkh W ~ O U S rates (Rh&). Rldr = 8 Kbps. ald , = ahdr = 1.

= Th& = 7 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.7 Admissible region of the 5 MHz CDU\ channel wi th various activity levels

(ahdr) for high data rate users. Rldr = 8 Kbps. Rhdr = 61 Kbps. nidr = 1.

,̂l& = ?hdr = 7 dB. . . . . . . . . . . . . . . . . . . . . . . . . - - - . - -

3.1 Inter-ce11 interference is caused by neighboring base stations to mobile ter-

minal :CIi in one sector of ce11 Co. . . . . . . . . . - . . . . . . . . . . . . .

Data rates of ;CI mobile terminals in the time-slotted structure of the CDMA

system in (a) TDM and (b) CDM mode transmissions. . . . . . . . . . . .

Rate-time diagram of al1 the active mobile terminals in a frame using the

RR scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Rate-time diagrarn of al1 the active mobile terminals in a frame using the

FF scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


ER scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


EP scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Data rate and power allocations to mobile terminals in the downlink. . . .

Throughput per sector as a function of the standard deviation ( O ) OC the

log-normal randorn variable t hat characterizes shadowing. . . . . . . . . . .

Delay as a function of the standard deviation ( O ) of the log-normal randorn

variable that characterizes shadowing. . . . . . . . . . . . . . . . . . . . . .

Throughput per sector as a function of transmission backoff probability. . .

System delay as a function of transmission backoff probability. . . . . . . .

Delay of 4 scheduling schemes: (1) RR. (2) FF. (3) ER and (4) EP. . . . .

Fked time coordination among neighboring base stations. . . . . . . . . .

Time coordination in sectored cellular network. . . . . . . . . . . . . . . . .A cellular network with four non-sectored cells and two possible cluster

patterns. . . . . . . - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

h cellular system \vit h 7 cells and 120" sectors: (a) Lxed transmission t ime

coordination, (b) the 21 sectors are named from la to i c and the reference

sector is also shown. (c) intedering sectors to the reference sector. (d)-(f)

g o u p of sectors where the maximum time share can add up to the frame

time (T). . . . . . . . . - - . . - . . - - . . . . . . . . . . . . . . . . . . . .

4.5 Allocation of time shares for three active mobile terminals using fixed and -- dynamic schemes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I a

4.6 An esample with ï cells and 11 mobile terminals: 6 different cluster patterns. 82

4.7 Incremental time share allocation in three iterations. . . . . . . . . . . . . 53

4.8 Time coordination among neighboring cells. . . . . . . . . . . . . . . . . . 84

4.9 Time reuse efficiency of FTSA and DTSA algorithms. . . . . . . . . . . . . 85

4.10 An example with 7 cells: (a) 11 mobile terminals (b) fair time share in ms. 91

5.1 Time-slotted structure wit h t ime dots and frames. . . . . . . . . . . . . . . 94

5.2 (a) Sectored-cellular system. (b) channel occupancy table for a sector using

the DCA scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10'2

5.3 Blocking probability for uniform traffic: the network has 100 sectored c d s

and 30 channels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.4 Blocking probability for siight ly (x= 10) non-uniform traffic: the nettvoi k has

100 sectored cells and 30 channels. . . . . . . . . . . . . . . . . . . . . . . 105

5.5 Blocking probability for estremely (x=30) non-uniform traffic: the network

has 100 sectored cells and 30 channels. . . . . . . . . . . . . . . . . . . . . 106

List of Tables

. . . . . . . . . . . . . . . . . 2.1 Signal parameters for class-1 and class-2 users 19

. . . . . . . 2.2 S u r n m q of the statistical moments of rsndom variables (i,/S,) 24

3.1 Fairness comparison of the scheduling schemes in terms of the ratio of the

maximum rate to the minimum rate . . . . . . . . . . . . . . . . . . . . . . 63

3.2 Data rates and time taken for two mobile terminals using the 1 scheduling

schernes (1) RR . (2) FF . (3) ER and (4) EP . . . . . . . . . . . . . . . . . . 64

4.1 Mean and normalized variance of the inter-ce11 interference for time reuse

cluster sizes of one and three cells . . . . . . . . . . . . . . . . . . . . . . . . 68

. . . . . . . 4.2 Sumrnary of' the performance of the FTSA and DTSA schernes 76

4.3 The range of time share that will be allocated by the FTSA-Mg and DTS.1-

Xlg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

. . . . . . . 4.4 Algorithm execution time for different sizes of cellular networks 58

5.1 Cornmon simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . 100

3.2 Channel occupancy table for the FCA scheme . . . . . . . . . . . . . . . . . 101

5.3 Traffic load comparison for cal1 blocking probability 0.01. . . . . . . . . . . 107

Chapter 1

Introduction The rapid growt h in wireless telephony and Internet applications bas opened the pot ential

market for wireless data services. The data services provided in the current wireless net-

works are limited to a fem Kbps. In this range of lotv data rates. services such as email

can be provided. The high performance portable devices with Internet capabilities (e.g..

laptop) demand a wireless communications system that ran provide high speed access.

The spectrum bandwidth being made available does not increase as fast as the growt h in

the data rates required by the applications. SIoreover. the data rate requirements for the

current applications va- with time unlike in telephony. Hence. the radio rcsource man-

agement will he very important in the future wireless access networks to satisfy the user

demand ahile efficiently utilizing the spectrum [t -31. In this t hesis. ive derelop and study

algorithms to allocate the wireless resources and scheduling schemes to control the channel

access rhar can yield betber pe~formante in providing high data ~ a t e services in CDXIA

syst ems.

1.1 Overview of CDMA Cellular Systems

In cellular qrstems, a larger area such as a city is divided into smaller coverage areas

called cells. Each ceil is covered by a base station deployed in the center of the cell.

The links from the base station to the mobile terminiils are called domlinks (or fonvard

links), and from the mobile terrninals to the base station are caIled uplinks (or reverse

links). The transmissions in the uplink and downlink are separated in frequency domain

using frequency division duplex (FDD) mode or in time doniain using time division dupIex

(TDD) mode. The FDD mode requires separate frequency bands for both uplink and

downlink. On the other hand. the TDD mode uses the same frequency band but alternates

the transmission direction in t ime.

The IS-95 system. which is a CDMA based wireless system. uses FDD mode. Here. the

available spectrum bandwidth is divided into a number of frequency bands. The bandwidth

of each frequency band is 1.25 MHz. The separation between the frequency bands used for

the uplink and the downlink is 45 hfHz. This separation is necessary to ensure the uplink

and downlink signals d o not interfere with each other. The cellular system concepts and

the historical developrnents of wireless networks are described in [5-111. In the rest of this

section we will discuss the modulation. mu1 t iple access. power control and spatial reuse of

frequency and time in CDiLIA systems.

In spread spectrum techniques. data symbols of one user are modulated by a unique

binary spreading sequence. which is usually a Pseudu-Noise (PX) code. Each modulated

data symbol is composed of binary chips that have a much shorter period than that of the

original data symbol. That is. the user symbol is spread into a transmission bandwidth

t h a t is much larger than the original signal bandwidth. The ratio of the bandwidth of

modulated signal to original signal is called the processing gain. The signal of each user

is despread (Le., eutracted) frorn the others at the receiver using a correlator keyed aith

the associated code sequence. The signals which do not match with the correlator code

sequence are not despread and contribute as interference. This technique is called Direct

Sequence-CDMA (DSCD MA).

The IS95 system uses DSCDMA technique. In this system, each user's narrowband

waveform is multiplied by a wideband signal that is generated by spreading codes consisting

of sequeoces of 6.1 chips. The entire sequence of chips is used to modulate the carrier during

each -bol period. resulting in a widened spectrum. The modulation techniques applied

in the uplink and donmlink of t his system are different.

In the downlink. a common pilot channel is used for time synchronization. Hence.

the transmission from a base station for different users in the downlink can be made

orthogonal. The IS-95 downlink uses DS spreading with orthogonal 64-chip Walsh code

sequences. There are 64 orthogonal Walsh sequences and each user is assigned a different

Walsh sequence. One Walsh sequence is reserved for pilot channel. In the 1s-95 uplink.

the transmissions from different users to the base station are difficult to be synchronized.

Hence? orthogonality arnong uplink transmissions is not preserved.

1.1.1 Multiple Access

In CD LM systems. transmission to/ froni different usen are separated using spreading

codes. In IS-95 downlink. different users are distinguished by the Walsh sequences assigned

to them. There are 63 Walsh sequences used for data transmission. Therefnre. a base

station can transmit for a maximum of 63 users simultaneously in the Qownlink. In the

uplink, different users are distinguished by the codes specific to their terminals.

In CDMA systems. since the sarne frequency band is shared by many users multiple

access interference (MAI) is introduced. There can be two types of MAI. One is the in-

terference caused by the users from within the ce11 of interest. also known as intra-ce11

interference. Other one is the interference caused by the users from outside the ce11 of

interest, also k n o m as inter-ce11 interference. The use of a set of orthogonal sequences in

the downlink ensures that the intra-ce11 interference is eliminated or at least minimized.

H m r . in a multi-path propagation environment the orthogonality ean not be presenred

among different paths. -4s a result, the advantage of using orthogonal sequences becornes

l e s significant in such environment. In the uplink. since it is difficult to maintain orthogo-

nality among uplin k transmissions intra-ce11 interference can not be elirninated. Moreover.

inter-ce11 interference can also not be elirninated in both uplink and downlink because time

synchronization among transmissions to/from different base stations is difficult to achieve.

1.1.2 Mobile Radio Propagation

The mobiIe channe1 varies a i t h time due to the changing environment and the rnovement

of mobile terminal. -4s a result. the receive signal power level varies wit h time. This is

cailed fading. There can be two types of fading namely shadowing and multi-path fading.

In shadowing, the signal pou7er variation is caused by contours of the terrain between the

user and the base station. Shadowing tends to Follow a log-normal distribution. Siulti-pat h

fading occurs when the signal arrives via different pat hs due to reflect ions of the t ransmitted

signal from the surrounding buildings and ocher structures. The different paths are received

with different del-S. The time difference between the first received signal and the last of

the refiected signals is called the delay spread. Exploitation of multi-path fading can be

achieved through the use of a Rake receiver if the delay spread is larger than a chip period.

When the received signal contains multi-path components which have a relative clelay of

a t least one chip. the received signal components are resolxble at the receiver and can

contribute to a better signal-to-interference ratio (SIR).

1.1.3 Power Control

The receive signal powver From the user close to the base station wvill be stronger than the

receive signal power from the user located near the ce11 boundary. Hence. the distant users

ntill be in disadvantage situation compared to the users in close pro'cimit. This is called

near-far problem. To overcome this problen. power control is used in the uplink. The use

of p m r controt can increase the sptern c a p a w and minimire the consumption of th r

transmit powver. The powTer control techniques used in the uplink and the doit-nlink are

different. In the uplink. the mobile terminals are power-controlled by the base station of

t heir o m cell. This is to ensure that the mobile terminals are received with the desired

power levels a t the base station. In a system with single-class of services such as voice. the

desired receive power level for al1 the mobile terminals are equal. Hom-ever. in a system

that provides services with different data rates and quality of service (QoS) requirements

the desired receive power levels can be different. In the dowdink, power control takes

the form of power allocation at the base station transmitter according to the needs of

individual users in their orvn ceII. For example. power controI can be applied by increasing

the transmit power to mobiles that suffer from escessive inter-ce11 interference. This will

usually happen when a mobile terminal is near the ce11 boundary.

1.1.4 Spatial Reuse

In non-spread spectruni multiple access techniques such as Frequency Division LIultiple

Access (FDBIA). frequencies used in a given ce11 are typically not used in irnrnediately

adjacent cells. This is done to ensure that the ceils using the same frequency wiii not

cause excessive mutua1 interference. In CDhlX systems the frecluency reuse cluster size

can be as small as one cell. That is, the universal frequency reuse can be applied not

only to al1 users in the same ceIl. but also to those in al1 other ciells. -4s a result. in a

multi-cell environment. CDBIA systems can have a larger capacity than the systems that

use non-spread spectrum multiple access techniques. where the frequency reuse cluster size

is larger than one cell. It was reported that the capacity can be further increased in few

CDMA systems [12-151 using a frequency reuse cluster size of three cells compared to one

ce11 in the case of non-uniformlÿ distributed trafic loads. These systems also eliminate the

problem for users near the ce11 boundary.

la) fbl

Figure 1.1: Frequency and tirne reuse clusters.

Figure l . l(a) shows the frequency reuse clusters of three cells. In frequency reuse

clusters. different frequency bands (f t , f2, f3) are used in different cells within a cluster

5

and reused in other clusters. In a cluster size of three cells, instead of using one frequency

for one ceIT with a totaI of three frequencies, it is aïs0 possibTe to use onIy one frequency in an

cells but in different time segments. The concept of time reuse clusters is introduced in [16].

It is similar to frequency reuse clusters. In time reuse clusters. al1 the cells use the same

freqiiency band. But, the time avis is divided into frames. and each frame is divided into

three segments ( t 1 9 t2, tJ) of time. Different segments of the frarne time is used in different

cells within a cluster. These time segments are reused in ot her ciusters. Transmission in

adjacent cells are made orthogonal in both time and frequency reuse clusters.

in the conventional CDMA system, where the sarne frequency band is used in a11 the

cells simultaneously. the frequency reuse cluster size is one ce11 and the time reuse cluster

size is one ceil as iveil. The adwntage of using the time reuse ciusters over the frequency

reuse clusters is that the uneven time segments can be allocated for the adjacent cells

when there is non-uniform traffic demand in the network. .As a r e d t . congestion due to

the non-uniforrn traffic load can be controlled. -4 dynamic transmission time coordination

among neighboring base stations (in downlink) is necessary to rnake sure that the uneven

tirne share for the neighboring base stations does not overlap.

1.1.5 Wideband CDMA Systems

The rates of data services provided by the IS-95 systems are limited to a few Kbps. There

have been many wireless research projects over the past few y a r s in the development of the

t h m t geueration wirefess technologies that proride n m capabilities such as higher data

rates and wider range of services. The tbird generation systems are based on Wideband

CDhIA (U-CDMA) [li-201 and Time Division CDMA (TD-CDMA) [18,21] systems. The

WCD?UIX l s t e m wilI be operating in separate bands for the uplink and domnlink in the

range of 2 GHz using FDD mode. The CVCDhIA system has a wider bandwidth in the

range of5 to 20 MHz compared to the 1.25 MHz bandwidth in the IS95 system. This wicler

bandwidt h provides the WCDhI.4 system wit h a supenor muit i-pat h resolution ability and

higher data rates capability. In order to support multi-rates. the use of variable spreading

gain CDM-A (VSG-CDM.4) [22,23] and multicode CDMA (MC-CDM-1) [22.24] schemes

are introduced. we consider the former scheme to achieve multi-rate data services. In

the VSG-CDMA scheme, the spreading ratio is reduced as the data rate increases. In

the MC-CDMA scheme. additional parallel codes are allocated as the data rate increases.

In the VSG-CDMA scheme. the signals with different bit rates are spread owr the entire

allocated bandwidt h. In t his technique, spreading gain and transmit power for various

services are different depending on their service requirements. It is shown in [25l that the

capacity of the MC-CDhI.1 and VSG-CDhIA are the same in non-fading channets. and

the MC-CDhI.4 has higher capacity in multi-path fading channels. Other new feat ures

supported in the CC'CDhI-A system are coherent detection in the uplink. fast power control.

adaptive antenna and multi-user detection [IO. 11.17-20,261. The TD-CDBIA systern can

be used in the unpaired bands in TDD mode. In TDD mode operations. it is possible to

change the duplex switching point in the tirne domain and move the capacity from uplink

to domlink, or vice-versa. depending on the capacity requirement of the uplink and the

downiink [1 1 2 11.

1.2 Radio Resource Allocation

The radio resources are transmit power and channels. The channels can be frequency

bands, time slots or spreading codes. In providing a connection to a mobile terminal the

systern has to assign a base station, a pair of channels for the uplink and the dowmlink.

and the appropriate transmit powers for the mobile terminal and the base station.

The task of any resource allocation scherne is to find the appropriate channel and

power assignments so that the required QoS c m be provided to as many mobile teminals

as possible. The radio resource allocation schemes need to know the measured interfer-

ence conditions. traffic characteristics and QoS requirernents. The interference conditions

depend on the number of interferers. their locations and the channel charactenstics. The

t r a c characteristics and QoS requirements depend on the type of senrices. For example

voice needs constant bit rate service, whereas a delay tolerant data application such as a

file transfer can be supported by best-effort services. The QoS requirements can be packet

deray and bit error rate (BER.). Fbice can tolerate some bit error but has stringent deIq

requirement. On the other hand. data applications such as file transfer of a medical image

can tolerate some delay but are sensitive to error. It is not possible to achieve a zero error

rate in mireless environment. However. the data applications which are ven sensitive to er-

ror can use forward error correction (FEC) or automatic repeat request (ARQ) techniques

to achieve lowcr error rates.

The QoS measures such as BER are directly related to SIR. The SIR is the ratio of the

power of the reference signal to the interference power seen at the receiver. The necessary

SIR to achieve the required QoS performance depends on the physical 1-er functions such

as modulation and coding of the sustem.

\ . \ - - i Base station

Mobile termid ,/-

(a) tbb

Figure 1.2: Interference for mobile terminal ikIi in (a) uplink and (b) downlink.

Figure 1.2 shows a cellular nettvork with a few mobile terminais and the base stations in

the cells. The mobile terminal of interest is LM,. In the figure. the links that contribute to

the intra-ce11 and inter-ce11 interference are shom by the solid and dotted lines respectively

The SIR for .\.I, measured a t the base station in terms of bit energy-to-interference ratio

(Eb /&) can be r,ritten as:

where LI; is the spread spectrum bandwidth, R is the data rate of JI, (the ratio IV/R

is caIIed the processing gain). S is the receive power of the signa1 frorn JI, at the base

station. I is the interference and q~ represents the background noise. In the uplink. I is

the sum of the intra-ce11 and inter-ce11 interference. Similarly. Figure 1 .P(b) shows the

downlinks that cause interference at mobile terminal Mt. In the downlink. the intra-

ce11 interference is eliminated by using orthogonal codes: hence. the interference (1) is

contributed by the surrounding base stations. In the dotvnlink. the SIR is rneasured a t the

mobile terminal. The corresponding Eb/Io is similar t o (1.1). but the term I does not have

intra-ce11 interference component.

In CDMA systems. when a mobile terminal transmits, it also causes interference to

othen. Hence. the operating power levels should be kept as srnall as possible. At the same

time. the power assignrnent for a mobile terminal in the uplink should aiso be high enough

to combat the interference caused by other mobile cerminals. Similarly. in the dotvnlink.

the fraction of the total base station transmit power for a mobile terminal should be high

enough to combat the interference caused by other surrounding base stations in the system.

The measured Eb/io should be greater than or equal to the required value in order to

have a successful communication and provide the required QoS performance. The receive

power (S) in (1.1) is the product of the transmit power and the rhannel gain. The data

rate R for a particular user can be increased by increasing the transmit power for the user

so that the required Ea/ Io is achieved. At the same tirne. the interference to the ot her users

also be increased. hence the Eb/Io experienced for the other users will be decreased. As

a result. the achievable data rates by the other users will be reduced. unless the transmit

powers for the other users are adjusted. Therefore? it is necessary to find a balance between

the transmit power and the interference.

1.3 Motivation and Thesis Outline

The access to back-bone networks can be provided by either wireline or wireless networks.

However. the rapid gowth of wireless personal communication is driven by the desire for the

change frorn wired fked place-to-place communication to wireless mobile person-to-person

communication. The current wirerine communication systems provide a variety of services

with voice. data. image and video. The future wireless access networks are erpected to

support services with a wide range of transmission rates and QoS requirernents.

Figure 1.3 shows a general view of the broadband rietwork that consists of wireless

access networks with a variety of mobile users. They can request voice services for their

hand-sets. low data rate services such as email. real-time stock quotes and weather report

updates for their hand-held devices and high data rate services such as web browsing and

video conferencing for their laptops. To date. we have seen voice and very low data rate

services in the wireless systems, however. wireless services that can bring video and images

to the lap-tops are still in the process of development .

Figure 1.3: Broadband wireless access netmrk.

The interest in high data rate applications is growing. At the same time. the conven-

tional wireless senices such as voice is also growing. That is. there n-il1 be an increased

demand for both voice and data services in the future wireless networks. In CDM-4 systems.

different data rates can be accommodated using different processing gains while keeping

the chip rate fked. Xloreover. the users with different data rates in a ceII need ciiKerent

receive power levels a t the base station. For example. a data user with rate ten times

higher than the rate of voice needs receive power approximately ten tirnes larger than the

receive power for voice user in order to achieve the same Eb/IO -4s the power allocation

for data users changes. the statistics of the interference seen by voice and data users also

changes. -4s a result. the system capacity of the integrated services is different from the

voice-only system.

In [X] the capacity of the SIC-CDiCI.4 system supporting integrated (voice and data)

traffic is analyzed. In MC-CDUA, high data rates are achieved by allocating several codes

to a single user for parallel transmission. This allows the mobile terminal using rn codes to

transmit a t a multiple rate of m times the nominal rate of a single code. A mobile terminal

using m codes experiences interference from it's own (rn - 1) codes. called self-interference.

An alternative to achieve multi-rates is to use a single code. but a t different processing

gains. In [28] two classes of traffic are considered: one is real-time service and the other

is reliable service. The spreading gains for the transmission of the reliable service users

are dynamically increased/decreased according to the multiple access interference levels.

The errored packets of the reliable service are re-transmitted. The optimal re-transmission

probability and the optimal spreading gain are found. and the admissible region of the two

classes of traffic are also found for a single ce11 system. That is. inter-ce11 interference caiised

from other cells is not considered. The system capacity is analyzed also for DS-CDIIA

in [29-321 and for SIC-CDiLIA in [33-351.

In Chapter 2. we focus on the integration of multi-class services such as voice and

high data rate in the uplink of the CDhl-1 system. In multi-class sertices. the users will

have different activity levels. Eb/Io requirements and transmission rat es, and consequent ly

different transmit power levels unlike in the traditional voice-only systems. We End the

admissible region, that is the number of multi-class users that can be simultaneously ad-

mitted in the system. Subsequentb we decide on the services t hat should be integrated

and separated in providing multi-classes of services for the performance henefits at the

system level. LVe also address the issue of interference contributed by high data rate users

on low rate users, and propose a channel access technique for delay insensitive data users

to increase the overall system capacity. We also show that the performance deteriorates if

high data rate and voice services are integrated into a single CDbI.4 system. Hence. it is

beneficial to separate high data rate services from voice services.

Next. me consider a system with only high data rate lnternet users. They are l e s

sensitive to delay. It is worth noting that the Internet users demand much higher data rates

in the downlink for downloading purposes compared to the low rate requests generated on

the uplink. Therefore. the capacity of the high data rate wireless Internet systenis will

be limited by the capacity of the downlink. -4s a result. we focus on the ciownlink in

Chapters 3' 4 and 5. We study how the resources such as transmit power and transmission

time should be allocated for various users to achieve performance benefits.

The CDbl.4 system is originally introduced for al1 the users to share the aame frequency

band simultaneously. Indeed. i t is code division multiplexing (CDhI) mode transmission

in downlinks. However. the data rates of the applications have increased much faster than

the increase in the bandwidth spectrum being made available. .As a result. time division

multiplexing (TDRI) mode transmissions in which only one active user accesses the channel

at a time is considered as an option to provide high data rates. We consider both TDN

and CDM mode transmissions.

High data rate allocation schemes for domiink are studied in [36-383. These schemes

consider only the TDhI mode transmission. In [36.37]. users are categorized into separate

classes and data rates are allocated according to their measured Eb/& levels. The time

slots are allocated by considering different latency levels for different classes of users. In

these schernes, the uneven service achieved by the various users may persist indefinitety

depending on the receiver mobility- An inter-ce11 coordination scheme to reduce the inter-

ceii interference is discussed in [381 for high data rate services. This scheme improves the

fairness in allocated data rates while reducing the overd system throughput. A n optimum

downlink power allocation is analyzed in [39] by rninimizing the normalized effective potver

when ARQ protocol is employed. High data rate access techniques are analyzed in (-101

&O for the uplink of CDMA cellular systems.

In Chapter 3. our focus is on the downlink performance of the CDMA system with

high data rate users only. We consider uniform traffic demand. thus it is reasonable to

set equal transmit power a t al1 the base stations. We propose and study a rate allocation

scheme for a CDMA system wi t h a time-slotted structure consist ing of variable-lengt h t irne

slots and frames. We show that the system perforrns better in TDU mode transmission:

consequently. a control mechanism is necessary to provide access to al1 the active users in

the system. We also propose and study scheduling schemes that can control the channel

access for high data rate users in the downlink. CVe compare the t hroughpiit and the dela';

performance of such scheduling schemes. CVe also show that the performance degrades

severely in adverse shadowing condit ions, and propose a scheduler t hat improves the system

performance significant ly by delaying the transmission for such users unt il the conditions

improve. In this thesis, we consider the fading due to shadowing only. ic'e do not consider

multi-path fading. The gain from the above mentioned scheduler can be tiigher if we can

keep track of multi-pat h fading and schedule transmissions accordingly. Hoivever. knowing

the channel conditions is not that easy in fast fading entironment.

In Figure 1.3. we can see that the traffic demand in the network varies s i t h time and

space. The non-unilormit- in the traffic distribution can lead to increased cal1 blocking

and poor service quality in the highly concentrated cells. Therefore. the network should

adapt to the changes and d ~ a m i c a l l y allocate the resources in order to achieve a better

utilization of the scarce radio resource. We consider a time reuse cluster size of three cells

in Chapters 4 and .S. In non-uniform traffic demand. keeping equal transmit power at al1

the base stations is not efficient. The transmit power can be increased in ce11 sites where

the traffic demand is high. It is also quivalent to allocate larger fraction of the frame tirne

to the cells nrit h higher t r a c dernand. The fraction of the frame t irne allocation for each

base station can be found according to the timely and spatially varying trafic demand so

tha t the transmission tirne in the adjacent cells do not overlap. If the priority levels of al1

the users in a ceII are the same. then they can be aIiocated a n equaI time share.

In Chapter 4. we consider a system that has a time reuse cluster size of three cells.

We focus on the fairness in allocating time share for data uses in the non-uniform traffic

environment. We propose and study a dynamic transmission t ime coordination algorit hm.

that allocates maximum possible time share t o the users in heavily concentrated areas

such as hot-spots, to maintain fairness in allocated data rates. We also compare this

algont hm with a fixed time share allocation algorithm in terms of time reuse efficiency and

also discuss various implementation issues. The proposed transmission tirne coordination

algorithm is suitable for data services t hat are delay tolerant .

Real-time services such as video telephony require a constant bit rate high speed service.

In providing real-time services. it is necessa- t o guarantee a fked amount of time share

in each frarne so that a constant average bit rate can be rnaintained over each frarne.

A guaranteed time share has t o be allocated for the entire duration of the connection.

hforeover, the number of simultaneous connections that can be provided for high data

rate applications will be small. As a result. call blocking is a major issue in real-time

high data rate services. Various time slot allocation schemes are studied in the literature

and sumniarized in [-Il]. These schernes wvork ive11 either in uniform or non-uniform traffic

environment.

In Chapter 5. ive fociis on reducing the call blocking probability for real-time high

da ta rate services such as video telephony in the non-uniform traffic environment. We

propose and study a time slot allocation scheme that can take into account the time and

spatial variations in traffic when assigning time slots to new cal1 arrivais. This novel scheme

efficiently packs the time slots and reduces the cal1 blocking in cellular networks.

In Chapter 6. the key results of Chapters 2-5 are re-emphasized. and the concluding

remarks are given. The possible directions for further improving the systems of considera-

tions are also addressed.

1.4 Thesis Contribution

In this thesis. we

1. analyze the integrated system of multi-class services for the CDhIA uplink. to identify

the services t hat can be integrated and separated to benefit at the system level. and

propose a technique that can provide an efficient integrated system of los rate and

high rate services by controlling the channel access of delay insensitive high data rate

users (421.

2. propose and study a rate allocation scheme for the downlink of CDM-A sytems. and

show that a better delay performance and higher data rates can be achieved when

only one user is alloaed to access the channel at a time compared to the conventional

CDM.4 systems [43],

3. show that the scheduling schemes that allocate the resources according tn the channel

fading conditions using the time-slotted packet mode transmissions can achieve better

t hroughput and delay performance. and propose a technique t hat can significant ly

irnprove the CDMA system performance while delaying the transmission for the users

t hat experience adverse channel condit ions [U].

4. propose and study a novel dynamic transmission tirne coordination algorithm that

takes into account the timely and spatial variation in user traffic demand. to improve

the time reuse efnciency and maintain fairness in allocating time share for delay

insensitive high data rate users [45j1

5.. propose and study a novel time slot assignment scheme for high data rate real-time

services. that unifies the advantages that exist in the k e d and the dynamic schemes

in order to efficiently pack and dpamically allocate the time slots so that the cal1

blocking is lower in al1 t raffic conditions [46].

Chapter 2

Channel Access Control Schemes for

Integrat ed Services

2.1 Introduction

Voice and data t raffic have different charac t erist ics and requirements. B y providing each

voice terminal with a speech activity detector. it is possible to distinguish when the source

is active or silent. -4 better channel utilization can be achieved if the voice tenninals do

not transmit during silent periods. The main service constraint for voice communication is

to guarantee low delay in delivering speech packets. If the transmission delay of a packet

exceeds a specified limit. the packet will be discarded and the quality of service will he

degraded. On the other hand. some data communications are less sensitive to packet

transmission delay Furthermore. data transmission will occur in burst that wi11 occiipy

the channel continuously for the burst period [47].

In this chapter: we consider the uplink of the WCDMA system wit h the muit i-class (e.g..

voice and data) users mith various Eb/Io requirernents. We obtain the system capacity in

tems of the maximum number of admissible users fiorn various classes. We study channel

access control techniques that can improve the system capacity of the integrated services.

We also anaiyze the system to determine what types of services to be integrated and what

to be separated to achieve performance benefits a t the system level.

We consider variable spreading gain CDMA system to provide rnuItipIe data rates. The

signds with different data rates Riil1 have diEerent processing gains and receive power levels.

For example. if two signals with rates RI (= R) and R2 (= nR) are to be transmitted.

they will have a spreading gain of Gl (= G) and G2 (= G i n ) respectively. Their desired

receive powers will be approximately Si (= S) and S2 (= nS) [93] respectively to achieve

the same Eb/ Io. Since the receive power levels of various users are different . the intra-ce11

interference and the inter-ce11 interference st atist ics are also different in the C'SG-CDMA

system compared to the conventional CDhl-4 systems where al1 the users have the same

data rate and receive power levels.

The rest of this chapter is organized as follows: The system mode1 for the uplink of a

VSG-CDMA systern is given in Section 2.2. Analysis of the systern capacity is shown in

Section 2.3. Numerical results are discussed in Section '2.4. Finally. Section 2.5 summarizes

the chapter.

2.2 System Model

In this section. we discuss the cellular system. traffic and interference models.

2.2.1 Cellular System Model

The system has the regular hexagonal cells. The mobile terrninals use omni-directional

antennas and the base stations use three directional antennas. hence operating with three

sectors in each cell. Let us denote the ith mobile terminal Mi and j th base station Bj.

Figure 2.1 shows the cellular system mode1 considered for the analpis. Here. Bq is the

base station that the mobile terminal .CI, is communicating Nith. and Bp is the reference

base station where we estimate the interference from the mobile terminals which are sewed

by the base stations in the surrounding cells.

Figure 2.1: The mobile terminal .IIi is cornrnunicating with the base station Bq and causes interference at the reference base station Bp.

2.2.2 Traffic Mode1

We perforrn the capacity analysis for a system with ttvo classes of traffic. Our analysis is

generic in the sense that the two classes of traffic can be of any type. For example. class-i

can be voice and class-2 can be data. or class-1 can be low speed data and class-2 can be

high speed data. In Our analysis. we incorporate the fact that different classes of traffic

have different data rates. quality of service (QoS) requirements and levels of activity. As

mentioned in Chapter 1. the QoS requirernents in general can be BER. delay and del-

variation. Here, we consider BER as the only QoS requirernent for the service. The analysis

can be easily extended for a system with many classes of trafic. The parameters for class-c

users are surnmarized in Table 2.1.

We assume that the users from each class are placed wit h uniform distribution in each

cell. The capacity of the systern with two classes is defined by the masimum number of

admissible users in a sector from class-1 (x) and class-2 (Y2) respectively Since there

are three sectors in each cell? the total number of users that can be supported in a ce11 is

3 ( N L + +V2). The area ûf the hexagonal ceU with a unity ce11 radius is 3&/2 units. Thus

the density of class-c users per unit area. p, = 3.

r

1 Rate 1 R, 1

1 Activity factor ( n, 1

Spreading gain Xumber of users/sector

BER requirement Eb/ Io requirernent

Receive power

Table 2.1: Signal parameters for classl and class:! users.

-

GC 'i, e c

"/c

S c

2.2.3 Interference Model

The channel gain from mobile terminal .LI, to base station B, can be written as:

where rt3 is the distance between mobile terminal .\I, and base station B,. p is the distance-

power-law coefficient and ( is a Gaussian random variable. that characterizes shadowing,

with zero mean and standard deviation a [30]. The value of p is 2 if there is a direct line

of sight. In practical mobile radio environment. the value of p is in the range of 2 to 5

which is caused by the fact that the radio waves are reflected and partial- absorbed by

objects between receiver and transmitter and by the surface of the earth. Usually p is

taken to be 4 [32]. A perfect power control is assumed in each cell. That is. the signals

from the same class users that are power controlled hy a base station have the same receive

power when the users are active, Similarly. the users who are connected to the other base

stations are power controlled by their base stations. Figure 2.1 shows the reference base

station (B,) where we would like to estimate the maximum number of admissible users.

and the base station Bq that the mobile terminal .II, is communicating with. The distances

betweeo mobile terminal A L I i and base stations Bp and Bq are r,, and r,, respectively. The

respective Gaussian random variables that characterize shadonjng are ci, and cl,. The

transmit power from mobile terminal .\fi of classc must be adjusted so that the receive

power at base station Bq is Sc. The channel gain from mobile terminal JI, to base station

Bq is given by h,, (= r&41~(t1q11"). Hence, the transmit power frorn mobile terminal JI, of

class-c can be given by SJh,. Mso. the channel gain from mobile terminal .\fi of class-c to

base station B, c m be written as hi, (= r $ 0 ( { 8 p / ' O ) ) . NOW, the interference that mobile

terminal .II, of class-c will cause at the base station B,. I,(r,,. r , , ) . c m be written as:

2.3 VSG-CDMA System Capacity

The QoS measure ive consider in this analysis is the BER which depends on the ratio of the

signal bit energy to interference (&/ Io ) . We analyze the VSG-CDLU uplink capacity and

study the possible improvements in the capacity by controlling the channel access of the

data users. The Eb/Io requirements can be different for different classes of traffic. Now.

let us write the expression for Eb/io for class-l and class-2 as:

where 7, and are the &/IO requirement and the activity variable of the class-c user

(iCI,) respectively. The desired receive power level of a class-c user is Sc. In (2.4). the

numerator is the processing gain which is defined by the sp-stem bandwidth diiided by the

rate of the class-1 user. The denominator shows: (1) first term is the intra-ce11 interference

from its own class w r s , (2) second term is the intra-ce11 interference from other class users.

(3) third term is the inter-ce11 interference from the same class users. (4) fourth term is

the inter-ce11 interference from other class users and (5) Iast term is the background noise

denoted by q . Al1 quantities in the above have been normalized by SI in (2.4).

2.3.1 Interference Analysis

In (2.4) and (2.5) the inter-ce11 interference components such as I l /Si. I l /S2. IÎ/S2 and

12/S1 < and the activity variables such as qlTi and . w ~ , ~ are random variables. Activity

level is rnodeled as a binornially distributed random variable with Pr& = 1) = a, and

Pr(& = 0) = 1 - a,. We assume air, for voice (a,) is 318 and for data (ad) is 1. The inter-

ce11 interference component IJS, from a particular user .Lii in the other cell is represented

by (2.3). We can evaliiate statistical moments such as rnean and variawe of I I /S i and

12/S2 by integrating IJS, for al1 the same class users over the cellular network with a few

tiers around the ce11 of interest. Once ive find the statistical moments of 1, /Si and b/S2.

we can easily eialuate the statistical moments of II/S2 and 12/S1 using the relationship

between Si and S2.

Frorn (2.3). we can write the inter-ceil interference power normalized by the recrive

power for class-c user :II, as:

Here. &, and 6, are Gaussian random variables nith zero mean and a standard deviation.

We also assume and C, are independent. Hence, (ci, -cl,) is a Gaussian random variable

Kth zero mean and '20' variance.

In (2.6) we assumed that the user Mi is communicating with base station Bq and not

with base station B,. This will occur if hiq 3 hip. that is:

Let us define a function d(.) as:

If di = 1. user JIi will comrnunicate with base station Bq, and mhen active will cause

interference at base station Bp equd to:

where q is the ceIl site index that satisfies:

That is. JI, is a user that is not connected to base station Bp, ratlier it is an interferer.

Furtherrnore, user ;lIi is connected to the hase station to which it has the targest channel

gain, equiw-alently the lowest attenuation.

From (2.9). we can write the ratio of the total inter-ce11 interference (1,) to the signal

power (Sc) for each class-c as:

Non.. we can mi t e the mean (p,) of the random variable IJS, as:

4

p.. = E ($1 = acpc // (:) E { ~ o ( " P -"q)/lo Qi (Crp - r i p / r i q ) ) d-4.

as follows: Let us define f (" ) f NP

Csing the definition of di(.) and noting that (S ip - C,) is Gaussian with zero mean and a

standard deviation of m. f (FI cm be eialuated as follows: 'P

In (2.15). Q(.) is defined as:

By numerically evaluating the integral in (2.12), we can find the rnean of the normalized

inter-ce11 interference components.

Sirnilarly. the variance of I, / Sc (ozc) can be nnitten as:

ivhere g (2) is defined as follows:

So Far. we have looked at the mean and the variance of I&. We Céso need the mean

and variance of the other random nriables (IL/& and 12/Si). ivhich can be found by using

the relationship between Si and S2. For example, mean of Il /S2 can be written as:

and by looking at (2.12). mean of 12/S2 can be written as:

Furthermore, variance of I I / S 2 can be written in terrns of the variance of II/Sl as:

Hoivever, the variance of 12/S2 cannot be written in terms of the variance of I1/Sl because

of the terrns a, and a: in (2.17). Le., it is not linear on a,. Therefore. the variance of h / S 2

(oh) has to be e d u a t e d separately using (2.17).

Table 2.2 summarizes the statistical moments of the random variable Ic/Sc. Here.

pl, = EII1 of, =Var[li /Si] and o&=Var[Iz/Sz]- Al1 the other statistical monients can

be written in these three terms as shown in Table 2.2.

1 . 1 Mean 1 Variance 1

Table 2.2: Sumrnary of the statistical moments of random variables ( Ic /Sc ) .

2.3.2 Bit Error Probability Analysis

An adequate performance such as (BER< 10-~) can be achieved on the uplink a i t h

&/Io 2 7dB [31]. Consequently, the required performance is achieved nith probabil-

ity P = Pr(BER< 1 0 - ~ ) = Pr(Eb/Io 1 5). Now, we want to find the maximum number

of users from each class per sector that can be supported such that the P > 0.99. i.e..

Pr(BER< 10-~) > 0.99 which is equivalent to Pr(BER2 1 0 - ~ ) 5 0.01. The system capac-

ity in terms of the number of clziss-1 and class-2 users per sector that can be supported

such that the achieved Eb/Io is greater than or equal t o the required Eb/Io ((Eb/Io),) 99%

of the time.

According to (2.4) and (2.3). Pr(Eb/Io 5 (Eb/lo),) depends on the distribution of activity

variable (&) and the inter-cell interference (I,). We can m i t e the equation for the bit error

probability in terms of the known variables for class-1 and class-2 users. using (2.4) and (2.5)

as follon?~:

and

where dl and d2 are as follows:

The bit e n o r probability for class-1 users depends on the number of active users from the

other c lass l users (XI - 1)? the number of active users from al1 the class-2 usen (-&) and

the inter-ceIl interference caused by the class-1 and class-2 users. In (2.24) and (2.25). Cr,

and w2.i are binomially distributed random variables. Xow we can simplify (2.24) and (0.25)

using the conditional probability rule:

xPr (C cl,, = k l ) Pr (C C ~ . E = h) .

and

In (2.27), I l /SI and I z / S l are independent Gaussian randorn variables. Hence. ( I I + 12) /Si

is the sum of independent Gaussian random variables and has a mean pl and standard

deviation al. Here.

Similarly in (2.28). (Il + 12)/SZ is the sum of the independent Gaussian random variables

and has a mean p.2 and standard deviation a2. Here,

Vie can re-rvrite (2.24) and (2.25) as (2.33) and (2.34) for class-l and class-2 users. respec-

t ively.

and

Now we can use the probability of bit error equations (3.33) and (2.34) to evaluate the

capacity of the system in terrns of (NI. A$).

2.4 Numerical Results

We consider a WCDiL1.A system that has a bandwidth of 5 N H z . \.Iè ass sur ne the voice

transmission rate is 8 Kbps. We consider data rates of 8 Kbps. 32 Kbps mhich we call

low data rates. and 64 Kbps. 128 Kbps and 256 Kbps which ive call high data rates. We

assume that the Eb/Io of ? dB is enough to achieve a BER of less t han 1 0 - ~ on uplink [XI.

The activity factor for voice is assumed to be 318. and 1 for data. The distance-power-

law coefficient p = 4, and the Gaussian random variable (<) that characterizes shadowing

in (2.1) has a zero mean and a standard detiation ctf a = 8 dB.

2.4.1 Mean and Variance of Inter-cell Interference

To find the mean of the normalized inter-ce11 interference components (Ic/Sc). we evaliiate

the integral in (2.12) numerically over the cellular network shown in Figure 2.2. Here. the

reference ce11 (dl) is surrounded by two tiers of square cells such as the one used in [48].

There are 25 cells in the system. Each of the cells has 3-vL class-1 users and 3 - class-2

users. We assume the interference caused by the users from outside of these 25 cells is

negligible.

Figure 2.2: Cellular network with 25 square cells. the reference ce11 is in the center.

First, ive find the distance from each point i in the cellular area to the reference base

station (B, in ce11 di). Then for each point i. we choose the ce11 site index q which satisfy

r , = {r ik): instead of the rule shown in (2.9). That is. if the base station is

assigned according to ('2.9). the geometry that d l keep al1 the users communicating with

the base station ni11 no longer have a regular shape such as a square or heuagon. but

some unknom shape. Therefore. in order to perform the integration, we assume that the

base stations are asçigned according to the distance. This açsurnption will not make a

considerabIe difference in the statisticaI d u e s 131 J. The mean of I,/S,. E[I , /Sc]. tliat we

e ~ l u a t e d using (2.12) is 0.637ncrV,. where a, is the activity factor and .V, is the number

of class-c users in a sector. Hence. the mean for voice users is 0.239.V". which is also very

close compared to 0.247:V. reported in 1311 and 0.2-llJ&, in [-Ml for voice usen.

Similarly, the variance of Ic/Sc. Var[Ic/Sc] that we evaluated is 0.082.\;; for voice users

and 0.05&Vd for data users using (2.17). The variance for voice class is very close to

the values, 0.078N,, reported in [31] and 0.076 Y, reported in [-Hl. In [XI. the mean and

variance are not expiicitly found. rat her the values were assumed from [X 1. The variance of

data class is found from the variance of voice class using the relationship: a& = (ad/n,)&.

This is not correct. because the non-Iinearity on the activity factor a, in (2.1;) does tiot

allow the evaluation of the variance of one class from another class. The variance of the

data class (0.208&) is very high compared to 0.052iVd that tve found.

New: we know the mean and variance of the inter-ceIl interference components depicted

in Table 2.2. Consequently. the mean and standard deviation of (Il + 12)/SI and (II +12)/S2

can be evaluated using (2.29)-(2.32). Hence. we can use the bit error probability formulas

(2.33) and (2.34) to find the combinat ions of the maximum number of .VI and X2 which can

achieve a BER of less than IO-''. Indeed, both (2.33) and (2.34) give the same maximum

nurnber of (NI . iV2) values. That is. ive need only one of the two formulas to evaluate the

capacity of the system.

2.4.2 Capacity of the System with Voice and Data Users

Figure 2.3 shows the admissible region of voice and data for three different data rates that

are 1, 8 and 16 times larger than the rate of voice. In general. the BER requirements

for voice and data services can be different: however. we assume both requires the sarne

Eb/& which is T dB on the uplink. The largest integers (Zi, .Nd) that give the probability

of bit error l e s than or equal to 0.01 are deterrnined. That is. a BER of 10-3 can be

achieved 99% of the tirne. The set of these pairs (Nu, Nd) determines the admissible region.

i-e, the combination of voice and data users below each line in Figure 2.3 c m be served

simul t aneously.

Figure 2.3: Admissible region of the 5 MHz CDhI.4 channel with roice ancl data iisers for various Rd/&. R, = 8 Kbps. a. = 318, ad = 1. 7" = ~d = 7 dB.

The rnavimum number of users in a voice-only system reported in (311 is about 36 for

the IS-95 system ai th 1.25 MHz bandwidth. From Figure 2.3. the maximum number of

voice users that can be allowed in a system with 5 MHz bandwidth is about 175 when

there is no data user. The four times wider system bandwidth can increase the capacity

by about four times. In addition, wider system bandwidth can have a higher rnultiplexing

gain. Hence. our results for voice-only systern (i.e., Nd = O) is consistent with what n*as

previously reported.

In Figure 2.3. we can see that the number of voice users that c m be allowed in the

system dropped much faster than the increase in the number of data users. When the

rate of data users is four times higher than the voice users. every data user allowed in the

systern will deny access to about 11 new voice users. When the data rate is 16 times higher

than the voice rate. only 1 data users can b e accepted with 22 voici. iisers. compared to

about 175 voice users in the voice-only system. This is rnainly due to the Fact that the data

users need a receive power which is much higher (about 16 times higher when R d / R , = 16)

tlian that for voice users and transmit nith a n activity factor about three times higher

than that for voice. When the rate of the data users further increases. and becomes much

higher than the voice rate. each data user that arrives and departs the system tvill rapidly

change the nurnber of amilable voice connections in the system. which is an undesirable

system behavior.

Data Users with Various Activity Levels

We assumed the activity factor for data to be one in order to ascertain the number of

simultaneous data connections t hat can be supported along wit h voice connections. In

fact, data can be transmitted in burst mode with a duty cycle Iess than one. By controlling

the data user's activity levels. the data can be transmitted with various dut' cycles.

Figure 2.4 shows the maximum number of voice and data users nith various data

activity levels (ad). where the data rate is four tirnes higher than the voice rate. Since the

data is transmitteti with a duty cycle less than one. the average data rate is not 32 Kbps.

For example. if the data activity level is 0.8. the average data rate will be 25.6 Iibps. The

lower the data activity the higher the number of voice users that can be accommodated

in the system. For example, in Figure 2.4. ahen the nurnber of data users is 16. the

number of voice users can be increased frorn 20 to 130 by decreasing the data activity level

from 1.0 to 0.3. However, mhen the number of data users is small. the number of extra

voice conn~ctions that can be proiided by tomering the actihjty levels of data users is not

significantly large. In providing integrated seMces of voice and data it is important to

provide adequate capacity for voice users because such usen still dominate the market.

-- O 2 4 6 8 10 12 14 16 18

Number of Data Usen (Nd)

Figure 2.4: Admissible region of the 5 MHz CDMA channe1 with various data activity levels (ad) . Rd/& = 4, R, = 8 Kbps. a, = 318, yu = yd = T dB.

Therefore, when the number of data users in the system is high. it mi11 be mise to control

the activity levels of data users to provide enough capacity For voice users. Sloreover. the

activity level of data sen*ices c m also be increased (decreased) when the activity levels in

the voice users are low (high) so that the overall system performance is not affected.

Data Users with Various QoS Reauirements

In the previous sections, we assumed that the BER requirernents for data and voice users

are the same, each requiring an Eb/Io of 7 dB. Howewr. few users within the data category

require a much stringent BER such as 10-~ due to its nature of application [IO]. This will

require an Eb/Io of greater than i dB. On the other haud? since few other data applications

can tolerate a relatively larger delay. the use of Turbo codes can bring the requirement of

Eb/Io lower to achieve the necessary BER 1361. We study the capacity of the system with

voice class that requires Eb/Io = T dB and data classes that require 4 dB. 7 dB and 10 dB.

Figure 2.5 shows the admissible region for voice users and data users with various Eb/lo

requirements. where the data rate is four times higher than the voice rate.

. . . ..........-.... .... : .......... : ....... : ............ : .......... :...* ...... : ......... : -*. . . * \. : I . . . *. . '. i , * . . . . ......... .,. ....S... h.. ...... .;. ........ .:. .....:.............. ,:. ........ .;. ....... -4 . 3. . . :\. . . b , . . \ . t . . *- : * . . : \ * . 4 0 1 ......... L.. ...... . A . . .......... ..Q.. ..:.. ........ ......... ;. ......... .,:. ...... .:. ....... .d

. - . . : * . - * 6 \ * : . %* : * . - * T '.: . * ...... ........ ....a... ..S..... ....... ........ ........ ....... ...... 2of. . .;. .:. .;. -9.. -1.. -1.. ;. :4&. . . A . 8 9 . - - . t - W. 6

* L - .

0: t L 1

O 2 4 6 8 10 12 14 16 18 Number of Data Uses (Nd

Figure 2.5: Capacity of the 5 MHz CDhl.4 channel nrith various Eb/Io requirernents for

For non-real time data applications with QoS requirements that is similar to the one

for voice (i.e., BER= 10-7). the required Eb/Io will be lower. Consequently. the admissible

region Ml1 be larger such as the one shown by the solid line in Figure 2.5. A larger number

of voice and data users can be accommodated in the system 6 t h data applications nhich

require lower Eb/Io. Moreover. each data user can be replaced by only a smaller number of

voice users such as 5 in Figure 2.5. Therefore, the effect of accepting a voice call or a data

call will not make a significant difference in the available number of connections. compared

to the system with data applications which require a higher EsIIo. In other rvords. Ive

c m Say that the voice and data with lower Eb/Io requirements can mix together tell in

int egated services.

On the other hand, for real-time applications which have higher QoS requirements such

as a lower BER. the required &/Io will be higher. Consequently, the admissible region will

be smaller such as the one shown by the dashed line in Figure 2.5. Here. each data user can

be replaced by a larger number of voice users such as 20 in Figure 2.5. The effect of higher

QoS requirements will be even worse when the data rate is higher. The dashed line ail1

become steeper as the data rate increases, and the system will reach a point mhere al1 the

voice users will be replaced by only a single high speed data user with high QoS. That is.

the system can provide eit her data-on- service or voice-only service. Therefore. real-t ime

data services with high QoS requirements do not integrate well with voice services. In

this situation, it would be rather useful to separate the high data rate services from voice

services.

2.4.3 Capacity of the System with Low Data Rate and High Data

Fhte Users

The Low data rate users are those that use applications thag ;equire few Kbps rate such

as needed for reading emails. stock quotes and weather updates. The high data rate iisers

are those that use applications such as large file (data, image and video clips) transfers.

Similar to the admissible region for voice and data users. the admissible region is found for

low data rate and high data rate users as s h o w in Figure 2.6. The activity factor for both

types of data traffic is assumed to be one. Here, the low data rate is 8 Kbps and the high

data rates are 64. 228 and 256 Kbps. The maximum number of data users with a rate of

8 Kbps that can be accommodated in the system is about 63 compared to 17.5 for voice

3 4 5 6 7 8 Number of High Speed Data Users (NMr)

Figure 2.6: Admissible region of the 5 hf Hz CDhIA channel wit h low data rate and high data rate users with various rates (Rhdr). Rldr = 8 Kbps. = ohd, = 1. ?Idr = ?hdr = 7 dB.

use- whereas voice has an activity factor three times les .

Various Activity Levels for High Data Rate Users

The admissible region is shomn in Figure 2.7 for low data rate users and high data rate

users with various rates and activity levels. l h l e n t h e number of high data rate users is

high, the increase in the available low data rate user connections by lowering the activity

Ievel of high data rate user is high. For exarnple. when .bd, isû. .Vld, can be increased

from 8 to 28 by lowering ah& from 1 to 0.5. The admissible region can also be increased

by lowering the activity levels of low data rate users. However. the consequent effect on the

..-........ ..... q ............ : ............. : ............. : ..:. ............ :... .-........ :. .:.--:.: . . 1

. $ * *

t . ; t I

0 ; *

1 2 3 4 5 6 7 8 Number of High Speed Data Uses (N,)

Figure 2.7: Admissible region of the 5 MHz CD&I.-\ channel with various activity Ievels

number of high data rate users. which require a receive power that is 8 times higher. will

not be significant compared to the previous case. Fu~thermo~e. the te~minak that are used

for low data rate applications such as reading real time stock quotes usually have limited

capabilities due to their size and price. On the other hand, the terminals that are used for

high data rate applications such as video clip transfer are more expensive and capable of

doing additional processing. Therefore, it is beneficial to adjust the activity levels of high

data rate users when they are man5 so that the available low data rate user connections

can be significantly increased.

Chapter Summary

W e analyzed the uplink capacity of a multi-class CDSIA system in a multi-ce11 environment.

We found the admissible regions of integated services of voice and data tvith various t r ans

mission rates. Eb/Io requirements and activity Ievels. Admissible regions were obtained for

a wideband CDhlX system with 5 MHz bandwidth. In an integrated servir^ of voire and

high speed data. a larger number of voice users has to be dropped to add one more data

user in the systern. because high data rate users need a receive power that is much larger

than that for a voice user. When the rate of data users further increases and becomes rnuch

higher than the voice rate. each data user that arrives and departs the system. will rapidly

change the number of amilable voice connections in the system, tvhich is an undesirable

system behavior.

The number of available voice connections is small when the niimber of high data rate

users is large. In providing an integrated service of voice and data it is important to provide

adequate capacity for voice users. Therefore. when the number of data users in the system

is high, it will be wise to control the activity l e~e l s of data users to provide enough capacity

for voice users. However. when the number of data users is small. the number of extra

voice connections that can be provided by lowering the activity levels of data usen is not

significantiy larger: thus. the activity levels of data usen should not be controlled.

-1 relatively larger number of voice and data users can be accornrnodated in a system

with data applications which require lower Eb/ lQ. Moreover. eacb data user can be ~eplaced

by only a smaller nurnber of voice users. Therefore. the effect of accepting a voice cal1 or

a data cal1 wi1l not make a significant difference in the available number of connections.

compared to the system with data applications which require higher Eb/Io In other words.

ive can say that voice and data with lower Eb/lo can miu together well in integrated services.

On the ot her hand, the high data rate services wit h real-time applications and higher Eb/lo

requirements do not integrate well with the voice senices. In this situation. it would be

rat her useful to separate the voice and data services.

The admissible regions for low data rate and high data rate usee are also founcl. The

effect of lowering the activity levels of high data rate users on the available number of

low data rate users is significantly high. Xloreover, the terminals that are used for high

data rate applications such as video clip transfei are usually capable of doing additional

processing compared to the terminals used for low data rate applications such as reading

red-time stock quotes. Therefore. it is beneficial to adjust the activity levels of high data

rate users when they are rnany. so that the availabIe low data rate user connections can be

significant ly increased.

Chapter 3

Rate Allocation and Scheduling

Schemes

In the previous chapter the performance of the integrated data and voice services in the

uplink was studied. From the results it can be seen that it is beneficial to separate the

high data rate services from voice services. Wireless Intemet access demands much higher

downlink data rates from the base station than the lowver rate requests generated by the

users in the uplink. In this chapter, tve study the systern performance of high data rate

services in the downlink.

3.1 Introduction

Data applications can tolerate some delay compared to voice and they are well suited for

packet mode transmission. In telephony. it is necessary t o maintain a constant average

rate throughout the connection time. However. in most data applications it may not be

important as to how much of rate is allocated in the different segments of time as long as

the required throughput is achieved with a reasonably small d e l - In telephony. transmit

power is the only controllable resource. In data communication. both the transmit power

and the transmission rate can be controlled. Different rates can be accommodated by using

different processing gains, while keeping the chip rate tiued.

Data usen have stringent QoS requirements in terrns of BER. The BER requirement

can be mapped ioto the required SIR. The SIR in terms of bit energ. to interference ratio

(&,/IO) measured a t mobile terminal Mt in CDbIX system downlink can be written as:

where iV is the spread spectrum bandwidth and R, is the data rate for mobile terminal

hl,. Si is the receive power level at i\Ii. 1, is the inter-ce11 interference caused by the

surrounding base stations a t .LIi (intra-ce11 interference is eliminated in the downlink by

using the orthogonal codes) and r) is the background noise. Here. receive power Si is the

product of the transmit power Pi allocated for mobile terminal SI, zt base station Bj and

the channel gain hji from Bj to The transmit power Pt and the data rate R, can be

adjusted so that the rneasured (Eb/Io) i is larger than or equal to the required value. The

base station that is serving the mobile terminal of interest causes interference to the mobile

terminals that are communicating with other base stations. while the ot her base stations

are causing interference to the mobile terminal of interest.

If a user requires a data rate (Rreq). it can be achieved by adjustiug the corresponding

transmit power Pi at the base station. The required receive power level Sreq can be written

as:

The required data rates for the users who experience higher level of interference can be

achieved by allocat ing higher transmit power. While increasing the transmit power to

one user, the interference to other users is d so increased. This in turn d l decrease the

achievable data rates to the other users in their ceus. As a result. it is not always possible

to achieve the required data rates by al1 the users in the system.

On the other hand, if the base station transmit power is Lxed. the achievable data rate

is limited and that can be written as:

The achievable rate will strongly depend on the inter-ce11 interference. In this chapter. we

assume a uniform user distribution in the cellular network and the transmit powr from

each base station is equall. The users with higher interference levels will receive lower

data rates. However. they get larger transmission time to receive the entire data parket.

We study the throughput and delay performance of such system with various scheduling

schemes.

Transmit power from each base station is assumed to be fixed. however. the fraction of

the base station transmit power allocated for each user can be controlled. The fraction of

the base station transmit power that is allocated for each user rvithin a cell çan be kept

equal, hence the users will get unequal data rates according to the interference levels a t

the receiver. It is also possible to change the fraction of the base station transmit power

that is allocated for each user according to the interference level so that the users rvithin

a ce11 can get equal data rate. We evaluate the system performance in both scenarios.

The rest of this chapter is organized as follows: The model for the donrnlink of the

t ime-slotted CDMA system and the analysis to compute the appropriate data rates and

power allocations in the fading cellular environment are discussed in Section 3.2. Yarious

schednhg schemes appropriate for this system are discussed in Section 3.3. Sirnnlation

results are given in Section 3.4. Finally. Section 3.5 summarizes the chapter.

System Mode1 and Analysis

In this section? we discuss the cellular system model, time-slotted structure of the CDMA

syst ern, transmission access modes and t raffic modeI.

'In the case of non-uniform user distribution in the system, the transmit power from the base stations can be adjusted accordingly to satis- the t r a c demand.

3.2.1 Cellular System Mode1

We consider a regular cellular system with hexagonal cells. The mobile terrninals use

omni-directional antennas and the base station use t hree directional antennas. Hence.

there are three sectors in each cell. The system contains B + t cells (Co to Cs) as shown

in Figure 3.1. Here. the reference ce11 is Co which is in the center and surrounded by cells

CI to Cg. The base station in ceIl C, is denoted Bj. Figure 3.1 shows a mobile terminal

(A-1,) in one of the three sectors in ce11 Co and the interfering base stations in the first

tier. Since mobile terrninals use omni-directional antennas, al1 the base stations cont ri bute

to the interference. There are JI number of mobile terminals in each sector. hence. 3-11

number of mobile terminals in each cell. The transmit power for each sector from base

station B, is denoted P'. The pilot signal is assigned a fraction (1 - 3) of the total base

station transmit power: the remaining fraction â is allocated to al! the mobile terrninals

in the cell. Hence. the effective transmit power from base station B, is 3 P,. The transmit

power for mobile terminal Mt in a sector in ce11 Co is denoted P,. We ccan write: Pz = g , 3 ~ o .

where gi is the fraction of the effective base station transmit power allocated for mobile

terminal Ai, in the sector in ce11 Co.

Figure 3.1: Inter-cell interference is caused by neighboring base stations to mobile terminal JI , in one sector of ce11 Co.

The channel gain From base station Bj to a mobile terminal -\,fi in a sector in the

42

reference ce11 is denoted hji! and from base station Bo is hoi CVe consider the same

mode1 for the signa1 transmission environment that is used in the previous chapter and

explained in Section 2.2.3. WC measure the system performance such as throughput and

delay for the mobile terminals in the center ce11 Co by considering the inter-ce11 interference

from the surrounding cells (B of them). We assume that the channel conditions do not

change for the duration of a frame.

3.2.2 Tirne-Slotted Structure and Transmission Modes

In the system of considerat ion, the time avis is divided into frames. CVe consider two access

modes for transmission such as: ( 1) CDM mode transmission where data for al1 the active

mobile terrninals are transmit ted simultaneously as in the t radi t ional CDSIA syst ern and

(2) TDhf mode transmission where data for each active mobile terminal is transmitted in

the consecutive time dots one after the other.

In wireline networks. to transmit f i e d size packets, it takes fixcd arnount of time

because the transmission channels are relatively error-free and not varying with time.

However, in t ime v a ~ i n g channel condit ions in wireless environment. the fked size packets

will take mriabIe arnount of time to be transrnitted. Hence. me consider a CDMA systern

\vit h a time-slotted struct urc consisting of variablclengt h time slots and frames.

At the beginning of each frame, the active mobile terrninals can request for data mith

different QoS requirements. The mobile terrninals will receive different data rates as s h o w

in Figure 3.2(a). according to their locations and fading conditions. In TDM mode trans-

mission. downlink packets are time-rnultiplexed and transmit ted wit h variable data rates

in such a way to optimize the system throughput. There will be -11 time slots in a frame

if there are hf data requests a t the beginning of a frame and al1 .LI users can be served

mithin the frame. We wi1l show in Section 2.4 that it is impossible for a11 .\f data requests

t o be fulfilled within the frame in order to maintain acceptable systern performance. In

that case. there will be fewer time-dots than the number of data requests.

In the CDM mode. al1 the active mobile terminals d l receive data together within the

t 4 *L I 3 d

t M d Timc F m e (0 = Time Slot (T) Time

C

i- Fnmc (FI * (a) Uni-access mode transmission (b) Multi-acctss mode tmsmission

Figure 3.2: Data rates of .\l mobile terminals in the time-slotted structure of the C D h U system in (a) TDbI and (b) CDBI mode transmissions.

frame as shown in Figure 3.2(b). We do not need time slots here or equivalently. we can

assume there is only one time slot in each frarne and al1 the active mobile terminals are

scheduled to receive data within that time slot. In this case' the lengths of the time slot

and the frame are the same. However? the lengths of different frames can be tlifferent. The

users can be given equal data rate regardless of their locations and fading conditions or

different data rates according to their locations and fading conditions using two different

scheduling schemes in this system with CDS1 mode as discussed later.

We assume that there are J I mobile terminals in each of the three sectors in each ce11

and they are unifomly distributed in the area. Hence. it can be reasonably assumed t hat

the transmit power from each base station is the same. We also assume that the data

needed to be received by the mobile terminds are waiting in the buffer at the base station.

That is, the users are requesting for data that is ready to be transmitted at the base

station. The size of the packets transmitted to the users within a frame is fiued. but the

length of the time slots and frames are variable according to the channel conditions and

user trafic demand. We do not consider rnobility in the analysis. Hoivever, we coiisider

mobile terminals with different user locations and varying shadowing conditions. which

caii simulate the effect of mobility? in evaluating the system performance. This is donr by

varying the üser locations and shadowing conditions for each simulation run.

3.2.4 Rate and Power Allocations

In this section? ive derive the constraint on data rates for the mobile terminals in the

center cell Co and their optimum power levels. The analysis is perforrnecl for the CDM

mode transmission case. The results for the TDM mode system. where only one user

accesses the channel in a time slot. can be easily obtained by limiting the value of the

number of mobile terminals (.II) to one.

The Eb/Io requirement of mobile terminal .\ri is represented in ternis of its minimum

tolerable ratio of signal bit energy to interference (&/Io), Y i . Mobile terminals can request

for different Es/Io values. The signal receive power at mobile terminal JI , (S,) is the

channel gain ( h ~ ) times the transmit power (P l ) . That is, Si = hotPt. The effective

transmit power for mobile terminals froni base station Bo is $Po. Hence we can write.

The Eb/lo measnred for a user of ioterest .II, in a sector of ceIl Co slioukl be greater than

or equal t o the required value (-ym) in order to achieve the nece- QoS. That is.

The first term in (3.5) is the processing gain W/&. where R, is the data rate of mobile

terminal Mm. The numerator of the second term is the the receive powr at mobile terminal

A&. In the denominator, the first term corresponds to the inter-ce11 interference from the

surrounding base stations' and the last terni corresponds to the background noise.

For each rnobiIe terminal. we wiT doca t e the transmit power (P , = S,/hûJ in such

a way that (3.5) holds and the total transmit power is minimum. The power allocation

problem in each time slot/frame is an optimization problem with the objective of maxi-

mizing the total throughput in the given time slot/frame subject to the constraint in (3.5).

It can be proven (see (49.501) that the optimum power or equivalently maximum capacity

allocation is achieved when al1 Eb/Io constraints are met with equality. Hence. for the

optimum solution:

vVe assume the total transmit power frorn al1 the base stations are the same. That is. P,.

is equal to Po, V j . By substituting Po from (3.4) into (3.6):

By re-arranging the terms in (3.7). we can separate Si into one side of the equation as.

There are M nurnber of equations with i\.I unknowns (i.e.. power level for each user).

in (3.8). Now. w can re-mite the constraints as a set of linear equations as follows in (3.9)

where Li is defined as: hi = 1-?, hj i . Here. hi represents the inter-ce11 interference to

mobile terminal Mi caused by the B number of surrounding base stations. and hi = hi/hoi

represents the norrnaiized interference to mobile terminai JI,. Moreover. hi which ive cal1

the interference index of mobile terminal :CI, can be written as:

By solving the linear equations in (3.9), we can find Sm which is the optimum receive

potver level for the mobile terminal AL. The closed form of Sm can be aritten as:

It is common to deal with the transmit power than the receive power. Here. the optimum

transmit power Pm is equal to Sm/hom The potver levels depend on the Eb/lo require-

ments, interference lcvels, data rates of al1 JI mobile terminals and the noise power. Once

we determine the data rate allocation for al1 the users. the power levels can be found

from (3.11).

Let us re-write Sm as:

We can see that the condition Cor the existence of such an optiniuni solution for the

constraint in (3.5) is that a11 power levels rnust be positive. i.e.. Sm 2 O. Ym. This

condition is met when:

The conditions stated in (3.14) cannot be mutually satisfied for al1 m. For example. when

JI = 2. the FoIlowing conditions in (3.14) must be mutuaIIy satisfied:

î?R2 h2 - hl 2 , and (F) hm2

> 3. ho 1

If one of the a b o ~ e is true then the other one cannot be true. However. the conditions

stated in (3.13) can be mutually satisfied. Moreover, in (3.13). if the first condition

@Pi (*) hi 5 8) is satisfied, the second set of -11 conditions niIl also be satisfied

because hm is always positive. Therefore, the necessary and sufEcient condition can be

written as:

If the condition in (3.16) holds for a set of mobile terminals with appropriate rates. then

they can be scheduled to receive packets simultaneously from the base station in a single

time slot/frame. Their power levels are specified by (3.1 1).

In the above derivation, we first found the optimum power allocations that are given

in (3.1 1) by barely satis-ing the &,/Io requirements for al1 the users. There the porver

levels depend on the data rates and interference indexes (h:s) . Then. we found the limits

on the data rates that are giwn in (3.16) by ensuring that al1 the power levels are positive.

The data rates depend on the the interference indexes. The derivation is done for the

CDhI mode case. The corresponding equations for the T D I I mode case can be found by

substituting .CI = 1 in (3.11) and (3.16). In the real implementation. the appropriate

data rates will be found k t and then the necessa- power levels. This ni11 be explained in

Section 3.3.3. At the beginning of a frarne, the mobile terminals will send the data requests

and the interference indexes to the base station. The appropriate data rates and the order

of transmission will be decided according to the scheduling schemes t hat are developed

from the constra.int in (3.16). This mil1 be diicussed ne--.

3.3 Scheduling Schemes

The users can be scheduled in different ways to receive data in the downlink that can make

a difference in the system performance. CVe will discuss and compare four such schemes.

Two of them are appropriate for TDhl mode transmission and the rest are appropriate for

CDh.1 mode transmission.

3.3.1 Scheduling Schemes for TDNI Mode Transmission

IR the TDM mode, the downlink packet transmissions are tirne-multiplexed and transmit-

ted with variable data rates in such a way to optimize the system throughput. Csers will

get different data rates according to their fading conditions. Since only one user accesses

the channel a t a time the constraint in (3.16) will becorne:

In time do t t,, mobile terminal iCI, will have a data rate R, represented by (3.17) and

the rest of the !CI - 1 active mobile terminals will have a data rate of zero. From (3.17).

we can see that a mobile terminal's data rate depends only on its location and the channel

conditions from its home as well as surrounding base stations using the TDhI mode. It

does not depend on the location or channel conditions of the other mobile terminals in the

sarne sector, because other mobile terminals w i l not be receiving at this particular time

dot. W e consider two scheduling schemes narnely Round Robin (RR) and Fastest First

(FF) for TDM mode transmission.

Round Robin (RR) Scheme

In this scheme. mobile teminals are scheduled to receive packets in the order that they

request for data. This is analogous to the first-corne first-serve (FCFS) scheme. When

more than one user in the system requests for data simultaneousl_v. each MI1 be allowed to

receive one after the other in consecutive time slots. The RR scherne that we consider here

does not serve each mobiIe terminal for a fixed arnount of time as in the conventiona1 case,

but serves to transmit a fixed size of packet in each time dot. The first mobile terminal

will start receiving its data with rate RI in the first time slot uritil it finishes receiving.

Subsequently. al1 .II active mobile terminals oill receive data in the .II time slots within

the frame as shown in Figure 3.3.

L 1 Rate of active mobile texminids

Figure 3.3: Ratetirne diagram of al1 the active mobile terminals in a frame using the RR scheme.

The achievable data rates by the mobile terminals in ce11 Co are given by (3.17). The

rate-time diagram in Figure 3.3 shows data rates allocated for .II mobile terminals in .II

consecutive time slots. The lengths of the JI time slots are tl.t 2.....td[. while the length of

the frame is x::, t,. Motice that the length of the h m e can \a- according to the nurnber

of requests and the fading conditions. LVe defiue the throughput of this system as:

C" &ti throughput = -

This system has two types of delays such as transmission delay and waiting del- for its

turn. The tirne difference betwen the moment the data request arrives and the starting of

next frame also contributes to the average delay. However, for simplicity we do not consider

this portion of the delay in our analysis. Let us recall our assurnption on the arriva1 process

50

that .U data packets are waiting a t the base station and ready to be transmitted. We define

the average delay of the system as:

Fastest First (FF) Scheme

This scheme is similar to the RR scheme but, the order the users will be scheduled to receive

data ail1 be different. The FF scheme will allow the user who can finish receiving the data

first to use the first time slot. This is analogous to the Earliest-Due-Date scheme [fil].

Similarly, this scheme will schedule al1 M active users in the increasing order of their time

taken for receiving data in the JI time slots. The corresponding rate-time diagram is

shown in Figure 3.4.

4 i Rate of active mobile terminals

Figure 3.4: Rate-time diagram of al1 the active mobile terminals in a frame using the FF scheme.

For the FF scheme, the throughput will remain the same as that for the RR scheme

because only the order the users access the channel is changed but the data rate and the

associated time do not change. However, the time delay of the FF scheme d l be different

from the RR scheme. The throughput and the delay can be defined by (3.18) and (3.19)

respectively for the FF scheme as weI1.

If the time dots are very small so that only a piece of the data packet can be received

then the choice of scheduling scheme will not make a substantial difference in the per-

formance. However. the proper choice of scheduling scheme can significan t ly improve the

performance if the entire data packet can be received within the time slot.

3.3.2 Scheduling Schemes for CDM Mode Transmission

In the CDM mode, al1 the mobile terminals that request for data will be scheduled to

receke sirnultaneously. Therefore. the data rate that should be allocated for one user

depends on the fading conditions of that user and the other simultaneous users in the same

sector, thus the rate allocation becomes more cornplicated. The constraint in (3.16) can

be re-written as:

In the TDhI mode schernes. since there is only one user receive at a time in a sector.

each user is transrnitted with the maximum transmit power. However. in CDS1 mode

schemes. transmit power in a sector has to be distributed among the active users. We

discuss two schemes namely. Equal Rate (ER) which assigns equal rates to al1 the active

mobile terminals with different transmit power levels, and Equal Power (EP) which aççigns

an equal amount of the base station transmit power to each active mobile terminal.

Equal Rate (ER) Scheme

In this scheme. al1 the mobile terminals are allowed to receive data simultaneously with

equal data rate ( R E R ) regardles of their locations and the channel conditions. For systems

that support multicast applications, this scheme is usehl because it is necessa- that al1

mobile terminals get equal data rate regardless of the channel conditions. The data rate

of the mobile terminals can be computed by replacing R, by RER in (3.20) as:

From (3.21 ) we can notice that a mobile terminal's receiving data rate does not just depend

on its location and channel condition, but it also dcpends on the other active mobile

terminals' locations and t heir channel conditions. The corresponding rate- t ime diagram is

shown in Figure 3.5. Since n7e assume equal packet size. the time taken for al1 the mobile

terminds turns out to be the same as shown in the figure. The throughput and the delay

of this scheme can be defined as:

throughput = JI x RER and delay = T.

A Rate for active mobile terminals ,,L ........................................ ~...-*,,,,----.---w,-uwuu.u-.w-..-.--.-.+-+.-+r ...... -.--.i.+ ,

Framc F. with tength T . , + ~ i r n z

Figure 3.5: Rate-the diagram of al l the active mobile terminals in a lrame using the ER scheme.

Equal Power (EP) Scheme

In this scheme. equal amount of the transmit power is allocated for al1 the users in a sector.

-4s a result. the data rates are allocated based on channel conditions- Mobile terminal ,'Ci, is

allocated a fraction (9,) of the effective transmit power (3 Po) from base station Bo. Hence.

Pi = gi,8Po. From (3.6) we can write:

Sm = - hjmq + miV CC' =1

The receive power level Sm also can be nrritten as: Sm = Ilo,. Pm = hhg,i3fo. By

substituting Sm into (3.23) and re-calling that the total transmit power (4) is equal for

al1 base stations Bj, we can arite:

If we neglect the background noise to find the fraction of the transmit power allocated for

each mobile terminal.

For eqtial amount of transmit power allocation, the fraction (9,) of the effective transmit

power for al1 the mobile terminals served by the same base station should be equal and

gi 5 1. Hence.

From (3.26) we can write the achievable data rate by each user as foHon-s:

We can see that (3.27) satisfies the constraint in (3.20). Therefore, the mobile termi-

nals served by the sarne base station can be provided with data rates given by (3.27) by

allocating equal amount of the transmit power.

The corresponding rate-time diagram is show in Figure 3.6, where the mobile teminals

------------ W.... O." y& ..* -- ='2 - 0.0 -TM f

F m e F -ia&

Figure 3.6: Rate-time diagram of al1 the active mobile terminals in a frame using the EP scheme.

get data rates given by (3.27) for the duration of Ti. At the end of T l . mobile terminal

Ml finishes receiving data because its data rate is higher t han the rest. For the durat ion

.... of T2: the mobile terminal Mm. E {Ai2, } will get data rate as follows:

3 I c- Ym'.

Dunng the time of T2, there are only i\.I - 1 niobile terminals sharing the entire base station

transmit power. i.e.. the .CI - 1 mobile terminals will get equal amount of the transmit

power. Similady. al1 the JI niobile terminals will be finished receiving data one after the

other in .CI time segments. The data rates for -II mobile terminals in A l time segments can

be found. When we calculate the throughput and delay of this system we have to consider

the different data rates of the same terminal in different time segments. Let us denote R,,

as the data rate for mobile terminal Mi in time segment Ts. The throughput and delay of

the EP scheme: which is sirnilar to that in the TDhI mode schemes. can be defined as:

115 CL &,A ~ ~ ~ 1 ( * ~ ~ + I. - 2)ti throughput = and delay = CL, t S ;'CI

B w rmtion

Figure 3.7: Data rate and power allocations to mobile terminals i ~ . the doanlink.

3.3.3 Implementat ion of Rate and Power Allocations

The base station will get the data requests and a report on the measurecl interference levels

that the mobile terminals see a t the beginning of the frame in the uplink control channels as

shown in Figure 3.7. M'hen the mobile terminal requests for a data that exists somewhere

in the back-bone network. the base station should first send the request to the data source

and get the data via the wireline network. Once the data has arrived at the base station.

it will be waiting at the buffer to be transmitted to the terminal which requested it. For

simpticity. nie assume the data that should be received by the mobile terminals are waiting

in the queue at the base station and ready to be transmitted.

The rate and power allocations for each time slot/frame are done in two phases as shown

in Figure 3.7. In the first phase. the appropriate data rates are found according to the

reported interference levels and the QoS requirements, and the scheduler ni11 be not ified

of which terminals d l be receiving data in the current time slot/frame. In the second

phase, the corresponding power levels are found and reported to the power leve1 adjuster.

Finally, the data Ni11 be transrnitted to the mobile terminals with the appropriate power

levels that can achieve the required QoS in the current frame.

3.4 Simulation Results

CVe assume that there are AI mobile terminals placed with uniform distribution in each

sector. The total transmit power from each base station are equal. Li-e assunie t hat the

surrounding base stations in the fint two tiers contribute to the total inter-ce11 interference.

Le.. B = 18. We consider the length of a data packet to be 312 bytes. The throughput

and delay depend on the data rate for each user and the time taken in receiving the

fixed size packets. The data rates depend on the normalized interference indeses (hi's)

which are represented in terms of channel gains. The channel gains depend on the mobile

terminal locations and the log-normal shadowing as ili (2.1). To find the average values

of throughput and delay. we ran the simulation with different mobile terminal locations

and fading conditions. At each simulation run. the appropriate data rates for the mobile

terminals were computed according to their channel conditions and Eb/Io requirements

using the appropriate formulas corresponding to the scheduling schemes. Then the mobile

terminals' transmission time were computed using the values of data rates and the packet

size. Finally. the average throughput and the average delay were cornputed froni the data

rates and the corresponding transmission t imes.

The system bandwidth is assumed to be 5 MHz as in the WCDhIA standards [181. We

assume the Eb/lo requirement for al1 the users are the same and the corresponding value

in the downlink is 5 dB. The value of 3 is 0.8. We assume p = 4 and a = 8 dB for the

signal transmission mode1 [3O].

3.4.1 Effect of Shadowing

Figures 3.8 and 3.9 show the average throughput and the average delay of the fastest-

first scheme for the number of users ranging from 1 to 10 as a function of the standard

deviation (a) of the shadotving parameter. Notice that a = O corresponds to a system with

Figure 3.8: Throughput per sector as a function of the standard deviation ( O ) of the log-normal random variable t hat characterizes shadowing.

Figure 3.9: Delay as a function of the standard deviation (a) of the log-normal random variable t hat characterizes shadoning.

no shadowing. As a increases from O to 8 dB, the system throughput decreases by about

90%, and about a ten-fold increase in delay is also observeci. This system performance

degradation due to shadowing is not desirable. We believe that this undesirable behiivior

of the performance is caused by a few users with severe shadowing conditions in the systern.

3.4.2 Transmission Backoff Probability and Performance Trade-

off

Providing senrices to those users with adverse channel conditions is a \vaste of resoiirces,

while there are other users competing for services with better channel conditions. hecaiise

none of them will be satisfied. The data rates that can be allocated for thc users with

adverse channel conditions will be very Iow according to (3.17) (5.21) and (3.27). Con-

sequently, the time taken to transmit a fixed size packet uill be very higti. If we do not

distinguish and delay the transmissions for the users wit h severe facf ing conditions. t hc

rates that will be allocated for other users in TDM mode transmission schemes will not be

affected according to (3.17): however, the waiting time until al1 the packets are transmit ted

in the current frame will be very high which will significantb degrade the system perfor-

mance. Moreover. the rates that will be allocated for the rest of the users will be very low

in CDbI mode schemes according to (3.21) and (3.27). Consequentl. the system perfor-

mance ntill be significantly degraded. Therefore it is beneficial to deIay the transmission

for users a i th severe cliannel conditions until they improve.

The channel conditions can become better in the following scenarios. The users may get

better contacts with the base station in the next frames while moving. If the user is not a

mobile, it is also possible to move the user terminal to another location within the vicinity

that may give better contact. since wireless access networks support portability. It is also

possible that the shadowing effects can improve itself a t a &xed point wit h t ime ahen the

surrounding environment changes. The point here is that the transmissions for the users

with severe shadowing conditions should be delayed unless there is no cornpetit ion or the

conditions are improved. We call this delaying event as transmission backoff. That is. the

systern baclis off the transmission for the users with severe channe1 conditions to the next

frame. I t is implemented by having a threshold on the transmission time. This will be

explained later. In nireless services there are two other such events narnely call blocking

and call dropping: however, the significance of transmission backoff is rninor for nomadic

users. On the other hand, it c m be very useful for the rest of the users in the network.

Figures 3.10 and 3.11 show the system throughput and system delay of a TDhI system

using the F F scheduling scheme as a function of transmission backo ff probability which is

defined as the fraction of the times the user transmission is dehyed in the system. For

a backoff probability of 0.05, a six-fold increase in average throughput is obtained and

the average delay is reduced by about 80%. The delay does not decrease significantly for

furt her increase in the backoff probability.

Figure 3.10: Throughput per sector as a function of transmission backoff probability

The average delay of the system with zero backoff probability is unreasonably high. This

is due to the adverse charnel conditions seen by a few users who get data rates close to zero

Figure 3.11: System delay as a function of transrnission backoff probability.

as a result. Consequently the time taken to transmit the fixed size packet will be very high.

which in turn brings the average delay to such high values. Once the data rate for a user

is computed. the expected transmission delay can be found. The channel access to a user

should not be allowed in the current frame if the expected transmission time is Iarger than a

threshold value. The operating point of transrnission backoff probability can be controlled

by choosing a such threshold d u e . In most of the Internet downloading processes. if the

information is not readily available, Le., takes longer delat., then the information becomes

useless and the users move on to reading or downloading other information. Therefore.

delaying the transmission for users that have an estimated transmission delay longer t han

the threshold is beneficial at least for the rest of the users. There is a trade-off between

the system performance and the transmission backoff probabiIity. If the del- t hreshold

is low (high) the systern will have better (worse) throughput and delay performance. but

the transrnission backoff probability wiii be high (low). It is important to note that the

transrnission backoff effect is not worse than c d dropping in adverse channel conditions.

However, if the channel conditions do not improve for a considerable amount of time then

it can lead to cal1 dropping.

3.4.3 Performance Cornparison of Scheduling Schemes

The throughput of the system does not change with the type of scheduling scherne because

Our schemes are work-conserving due to the variable-lengt h t ime-dot t ed structure, and the

assumption that the channel conditions do not change for the duration of each frame. The

throughput of the system is found around 0.7 Mbps For a transmission backoff probability of

0.05. This system will never be idle if enough requests for data are there. Since ive assume

variable-length time dots and frames, no time will be ivasted. However. in fixed-length

time dots and frames. which is the case in WCDhI.4 standards [Ml. it may not be possible

to pack data packets into standard time slots without some residual slot tirne rernaining.

As a result. a system with fixed-length time dots can become non-work-conserving.

O ' A - - 1

O 2 4 6 8 10 Number of MTs (M)

Figure 3.12: Delay of 4 scheduling schemes: (1) RR. (2) FF. (3) ER and (4) EP.

Figure 3.12 compares the average delay performance of the scheduling schemes. When

the number of users is one. ail four schernes have the s r n é delay because there is no need

for scheduling. The average system delay increases for d l four scheduling schemes with the

number of mobile terminais ( J I ) in a sector. This is due to the fact that. when -11 = 1.

there is no waiting to receive data. but when 111 = 2 the second user has to wait until

the first one finishes receiving. In general? the TDAI mode schemes perform better than

the CDhI mode schemes in terms of delay. In the TDbf mode schemes. the FF sclieme

performs better than the RR scheme. In the RR scheme. users are sen-ed in FCFS basis.

but in the FF scheme. the user that can finish receiving f i ~ t is senred first. Le.. the user

with the best channel conditions is served first. This will result in a smaller waiting time

for the other users that will be receiving data packets later in the same frame. -4s a result.

the average delay is smaller for the FF scheme compared to the RR scheme.

In the CDM mode schemes. the delay performance of the EP scheme is better than the

ER scheme. In the ER scheme. al1 the users tvill be finishing at the same time regardless of

their channel conditions. Hence. al1 the users have to wait for a long time to finish receiving.

However. in the EP scheme. even though al1 the users are allotved to receive simultaneously

they get data rates based on their channel conditions. As a result. the users with better

channel conditions will finish first. Moreover, once these users are finished receiving. the

other users will be allocated with extra transmit power which was initially allocated for

the users that have non- finished receiving. These factors help the E P scheme to have a

much better delay performance than the ER scheme. It is interesting to notice that the

EP scheme. which is a CDN mode scheme. performs better than the RR scheme. which is

a TDM mode scheme.

1 1 Schedulinn schernes 1

1 maximum rate to minimum rate 1 9.1 1 9.1 1 1.0 f 5.3 1 Average value for the ratio of

Table 3.1: Fairness cornparison of the scheduling schemes in terms of the ratio of the maximum rate to the minimum rate.

RR FF -ER EP

Table 3.1 shows the average value for the ratio of the maximum data rate to the

minimum data rate in the systern when the number of active users (Ml is trvo. The ER

scherne gives the most desired value of 1.0 because al1 the users get an equal data rate. The

value for the EP scheme is better than the RR and the FF schemes. That is. the CDM

mode schemes perform much better than the TDM mode schemes in terms of fair allocation

of data rates. It is interesting to note that the EP scheme has a better delay performance

than the RR scherne. and has a much betrer performance than both TDhI mode scheduling

schemes in terms of fair allocation of data rates. The E P scherne is indeed a combination

of the TDkI and CDII mode schemes, though we categorized it as a CDhI mode scheme.

The results show that a CDM mode scherne which allows only a few simultaneous users

to access the channel and also allows the users with worse channel conditions to access

the channel in TDM mode when the traffic demand is low. can achieve a better system

performance. That is? the throughput and delay performance will be better than that in

CDM mode schemes and bet ter in terms of data rates than the TDM mode schemes. The

few simultaneous users can be chosen for scheduling according to their channel conditions.

The Eollowing simple numerical example explains the throughput and the delay calcu-

lations of al1 four schemes in detail. Let us denote xi = yihi. Also. consider a system with

LU = 2 and assume the corresponding d u e s of xi are 1 and 10 respectivel- The data rates

for the twvo mobile terrninals can be found using (3.17), (3.21) and (3.27). Table 3.2 sum-

1 taken (ms) 1 t2

Data rate (Albps) Time

Table 3.2: Data rates and time taken for two mobile terminals using the 4 scheduling schemes (1) RR, (2) FF. (3) ER and (4) EP.

marizes the data rates and the time taken by al1 four scheduling schemes. For example. for

R1 R2 t l

the RR/FF schemes; the data rate (RI) for the first user is (y) 5 = 4.00 ivIbps according

4.00 0.40 1.02

0.36 0.36 11.4

2.00 0.20 2.05

O 0.40 9.21

to (3.17), and the time taken ( t l ) is = 1.02 ms. A11 four schemes have a throughput

of 0.73 Mbps which was found using (3.18),(3.22) and (3.29). Delay encountered using the

RR scheme cm be either 6.1 ms or 10.7 ms according to (3.19). and on average it will be

8.1 rns. Delays for FF. ER and EP schemes are 6.1. 11.4 and 6.7 rns respectively using

(3.19). (3.22) and (3.29). These numerical results are consistent wit h the simulation results

shown in Figure 3.12.

Chapter Summary

We analytically derived the appropriate data rates and transmit power levels t hat should

be allocated for the downlink in a CDCIA systern with time-slotted structure consisting

of variable-length time slots and frarnes. To compute the average throughput and delay

performance of the system, we ran simulation usiag the derived formulas with different

m o b h terminal locations and fading condit ions.

It is shown that the average throughput decreases by 90% and the average delay in-

creases ten-fold in severe shadowing environment compared to no shadowing. Hoivever. ive

showed that the average throughput can be iricreased six-fold and the delay can be reduced

by about 80% when operating the system with a transmission backoff probability of 0.05

which shows that the initial performance degradation in fading conditions must have been

contributed by only a small number of but very severely affected users. The requests froni

-5% of the user population with adverse channel conditions are deIayed untiI their channel

conditions improve. It is important to note that the transmission backoff technique is bet-

ter in terms of user satisfaction than the cal1 dropping technique: however, if the channel

conditions do not improve for a long time it will eventually lead to cal1 dropping. -4s a

result , these 3% of the users Ni11 experience either longer delays or cal1 dropping: however.

the rest of the users d l have a signscant improvement in the system performance. There

is a trade-off between the system performance and the transmission backoff probability.

The operating point of transmission backoff probability can be controlled by choosing an

appropriate threshold on the largest transmission delay for a data packet.

Vie compared the T D M mode scheduling schemes such as round-robin and fastest-

first, and the CDM mode schemes such as equal-rate and equal-power in terms of the

average throughput and delay. The average throughput of al1 four schemes turns out to be

the same, because our system is work-conserving due to the variable-length t ime-slot ted

structure. and the wumption that the channel conditions do not change for the duration

of each frame. However. the delay varies between the scheduling schemes and increases

tvith the number of users. The TDM mode scheduling scheme. namely the fastest-first

scheme, achieves the best dela? performance. In general. TDM mode schemes can provide

higher data rates and yield better del- performance. However, the CDM mode schernes

outperform the TDhl mode schemes in terms of fair allocation of rates.

Chapter 4

Transmission Time Coordinat ion

Among Base Stations

Introduction

In the last chapter? a CDAIA system with a time reuse cluster size of one ce11 ivas con-

sidered. That is, the same spectrurn bandwidth is used in each ce11 simultaneously, as in

the conventional CDhIA systerns. Moreover. the spectrurn bandwidth is also reused in the

sectors of each cell. There. inter-ce11 interference was contributed by al1 the neighboring

base stations. Hence. the transmission rates for users strongly depend on the interference

levels. The transmit power from each base station ntas considered to be fixed and the data

rate was optimized according to the channel conditions.

In non-CDMA systems such as AMPS and GSM, hard-orthogonality is maintained

among transmissions using different frequency bands or time slots. On the other hand. in

CDh1.A systems. soft-orthogonality is maintained among transmissions for different users

using spreading codes. However, the spreading factor becomes very small for high data rate

applications. Therefore, the soft-ort hogonality among transmissions becomes l e s effective.

As a result, for high data rate applications we consider a CDMA -stem wit h a reuse factor

larger than one.

Table 4.2: X[em and normalized variance of the inter-ce11 interference for time rwse cliister sizes of one and t hree cells.

In this chapter. we consider a time reuse cluster size of three cells to minimize the

inter-cell interference. This also eliminates the problem for users in the ce11 b o u n d a . Let

us recall (3.3).

"

p 3 4

The achievable data rate R, for mobile terminal iLli decreases as the inter-ce11 interference

increases. The time reuse cluster size of three celk reduces the inter-ce11 interference

significantly compared to the tirne reuse cluster size of one cell. When the cluster size

is three cells, interference is not caused from the first tier of six cells which othenvise

are the major source of interference. Moreover, half of the second tier of cells do not

also cause interference. Table 4.1 compares the mean and variance of the interference for

various distance-power-Iaw coefficients and time reuse cluster sizes of one and three cells.

The t-atnes in the table correspond to the channef gain contributeci by the &stance toss

onty. From the table tve can see that the interference is greatIy reduced for the tirne reuse

cluster size of three cells. As a result, the achiemble data rate mainly depends on the

receive power let-el, where the receive power is the product of transmit power and the

channel gain. Hence. it is possible to provide a fived rate to al1 the users by controlling

the transmit power.

When the time reuse cluster size is Iarger than one cell, the transmission time among the

base stations for each user have to be coordinated. Vilen a base station transmits. some

Int er-ce11 interference Cluster size one ce11 Mean 1.9817 1.3322

Cluster size three cells Variance

0.1159 0.3041

Mean 0.2433 0.0874

Variance 0.0066 O -0046

of the surrounding base stations should not transmit. The tinie reuse cluster size deter-

mines the number of neighboring base stations that are permitted/prohibited to transmit

simultaneously. A fixed time coordination among base stations is shown in Figure 4.1 for

a time reuse cluster size of three ceIls with no sectors.

Figure 4.1: Fixed time coordination among neighboring base stations.

-1 frame is divided into three time segments: Ti, T2 and T3. Here. Tl + T2 + T3 (= T)

is the duration of the frame tirne. The base stations of the cells marked by Tl. T2 and T3

Riif transmit in the respectire t h e segments as s h m in Figure 4. t (b). When the base

station in the center ceIl transmits, the base stations in the first tier cells and half of the

second tier cells wi1l not transmit.

In the uniform user distribution scenario. the time shanng mil1 be such t hat Tl = T2 =

T3 = T/3. In non-uniform user distribution scenariol. such a time coordination d l not

be efficient. For exarnple. if al1 the ceus in the first tier of the center ce11 have no active

'In Section 3.1. we mentioned that the base station transmit power leveb can be adjusted to satisfy the n o n - d o m user trafic demand- However, in this chapter, since we consider a time reuse cluster size larger than one ceii. we have the flexibility of adjusting the transmission tirne to satisl the non-uniform user t r a c dernand.

users, the base station in the center ce11 can transmit for the entire duration of a frame

time. Moreover. when the center ce11 has twice as many active users as in the surrounding

cells, it is not fair to allocate Tl = Tj3. In this chapter. we propose and study a dynamic

time coordination algorithm t hat allocates fair share of time for users according to the user

traffic demand in the dorvnlink.

/Y-. . Mobile terminal ',

/ h \ ,,'

Figure 4.2: Time coordination in sectored cellular network.

Figure 4.2 shows a system with three sectors in a cell. The tirne coordination among

the interfering sectors should be made such that the transmissions are orthogonal. In

Figure -4.2. the interfering sectors are shaded. When the base station transmits for the users

in the shaded sector of the center cell, the surrounding base stations should not transmit

for the users in the shaded first-tier sectors. The system capacity can be approximately

t hree-fold increased by reusing the time in all three sectors of the same ceIl. The tirne reuse

technique for the sectored cellular system will be explained in more detail in Section 4.3.1.

This chapter is organized as follows: The -stem mode1 is discussed in Section 4.2. The

effectiveness of the dynarnic schemes is shown in Section 4.3 along with the cornplexity

in running the algorithm. The dynamic time share allocation algorithm is developed in

Section 4.4. Simulation results are given in Section 4.5. FinaII- Section 4.6 summarizes

the chapter.

4.2 System Mode1

We consider the downlink of a sectored cellular system. As in the previous chapter. the

mobile terminals use omni-directional antennas and the base stations use t hree direct ionai

aotennas. The analysis and Our algorithm can aiso be used for the uplink and aiso for

cellular systems with varioiis niimber of sectors. For simplicity. we discuss Our algorithm

in Sections 4.3 and 4.1 for non-sectored cellular system. however. the simulation results in

Section 4.3 are given for a system wit h three sectors per cell. The time axis is divided into

fived size frames. Data will be transmitted in TDM mode. W e consider a t ime reitse ciuster

size of three cells. Figure 4.1 shows how the frame time is shared by the neighboring base

stations for transmitting data to mobile terminals in their cells. The transmission time

coordination shown in Figure 4.1 is useful when the user trzffic demand is uniform. and

it is not efficient in non-uniforni user traffic demand. In this chapter. we stiidy how the

transmission times can be coordinated in non-uniform traffic demantl for delay insensitive

Intemet applications. The active users request for data at the beginning of each frame.

In each frame. a segment of the frame time has to be allocated to each active user. It

should be noted tliat the tirne segments for the users within any cluster pattern should not

overlap. The frame time is k e d . but the time segment for each user is variable in size.

The users will get a segment of a frame time that depends on the user traffic demand at

that tirne.

4.3 Dynamic Time Coordination: Complexity and Ef-

fect iveness

The schemes that equally divide the fiame time among the cells in a cluster and allocate a

fked amount of time for the users are calied Fked Time Share Allocation (FTSA) schemes.

On the other band. the schemes that divide the frame time arnong the cells according to

the demand in the cells and allocate variable share of time for the users are calied Dynamic

Time Share .Allocation (DTSA) schemes.

4.3.1 Complexity of Dynamic Schemes

Let us first consider a system with four non-sectored cells as shown in Figure 4.3 and assume

a time reuse cluster size of three cells. Let us denote the time share t hat is allocated for

(a) Cluster L 1

\ /

'C (a) Cluster L 1

Figure 4.3: A cellular network with four non-sectored cells and twvo possible cluster patterns.

the cells -4, B, C and D as T4, TB, Tc and TD, respectively. The cells -4. B and C form

cluster Li, and the cells B, C and D form cluster La. The sum of time share allocated for

the cells in any cluster pattern should be l e s than or equal to the frame time. Hence. the

following constraints should be satisfied in cluster L and cluster L2 respectively :

T.I + TB + Tc 5 T and TB + + TD 5 T. (4.2)

In the FTSA scheme. T4= TB= Tc= T'= T/3. In DTSA schemes. each of the time

segments (T4, TB, Tc,TD) can get up to T as long as (4.2) is satisfied. For example.

the allocations can be: T4 = TD = T and TB = Tc = O. or T4 = TD = T/2 and

TB = Tc = T / 4 . Clusters LI and L2 overlap in cells B and C. If TB is equal to T. then T4,

Tc and TD will be zero. For ce11 Bt the other three cells are \vit hin the reuse distance (41,

i.e.: they can be covered within a single cluster. For ceil .-4 cell D is not within the reuse

distance. hence. both can be allocated up to T each. In Figure 4.3. cells B and C are

covered by only two clusters, but in a larger cellular network a ce11 can be covered by

six cIusters. Indeed, six different cluster patterns will overlap at the cell. hence. we cal1

these clusters as the overlapping clusters of that cell. In a larger network. for each cell. al1

the clusters that cover the cell should be checked to make sure the allocated tirne share

do not reach beyond the frame time. In Figure 4.3, since there are two possible cluster

patterns, there are only two constraints to be satisfied. In general. there will be a number

of constraints, which is equal to the number of possible cluster patterns in the network.

The number of cluster patterns are in the order of the number of cells.

In the case of sectored cells, the time share allocation for the users can be found by

writing the constraints similar to (4.2). Figure 4.4 shows seren cells in which the cells use

130" directional antennas, i.e., each ce11 has three sectors. There are 21 sectors in total.

Figure 4.4 (a) shows the fixed time share allocation among al1 the sectors. The same t irne

segment is not used in the adjacent cells that are within the first tier. Mon-ever. the rime

segment is reused in the adjacent sectors of the sarne ce11 because their antennas are directed

in different directions. The possible rnutual interference from the adjacent antennas of the

same ceIl in the overlapping segment of the antenna beams can be eliminated by using

orthogonal codes. The sectored system increases the capacity approximately by about the

number of sectors in a cell. There are 21 sectors in total and are named from la to Cc as

s h o w in Figure 4.4 (b).

Let us consider the sector la. To make sure that there is no interference to the users in

sector la from the first tier. Le.. the tirne segments used in 2c. 3c. -La. 5a. 66 and 76 (i.e..

t2=, tk, t4.: tSa9 t6* and tTb respectively) should not overiap with the time segment used in

sector la (tla). Furthemore, we can see that tZc and tTb should not overiap. however, t2,

c m overlap with tsb. In order to find the time share for the sector la (tl.). we can write

the following three constraints from Figures 4.4 (d) (e) and (f) respectively:

Figure 4.4: A cellular systern with i cells and 120° sectors: (a) fked transmission time coordination. (b) the 21 sectors are named from la to 7c and the reference sector is also shoivn. ( c ) intedering sectors to the reference sector. (d)-(f) group OF sectors rhere the maximum time share can add up to the frame time ( T ) .

Similarly. constraints can be aritten for each sector. The number of constraints in the

sectored system is approximateli' equal to the square of the nurnber of sectors per ce11

times the number of cells in the system. .As the size of the network grows the allocation

will become more complicated. Hence, it is necessary to develop a n algorithm that finds

the tirne segment of the frame for each user to transmit.

4.3.2 Effectiveness of Various Schemes

(a) (b) FISA (c) DTSA-A (d) DTSA-B

Figure 4.5: -Allocation of time shares for three active mobile terminals using fised and dynamic schernes.

Let us consider the example in Figure 4.5(a) with non-sectored cells. to understand the

different ways of allocating the time share in Kued and dynamic scenarios, their effectiveness

and faimess. There are three cells and three mobile terminals (Mi. -'CI2. J I3 ) in the system.

Since we consider a cluster size of three cells. the frame time (T) Ml1 be shared by the

three cells. In the FTSA scheme, the fiame time is equally allocated among the three cells.

'Sorne of the constraints written for Merent sectors will be identical.

Therefore. the time share for each ce11 is T/3. Hence, the fixed time share per user can be

as high as Tf6. The time share for the three users is shown Ïn Figure 4.5(b). The fraction

of frame time utilized for the FTSA scheme is 0.5 because the total time share allocated

is T/2. In FTSA? the ratio of the maximum time share to the minimum time share is one.

In the DTS.4-A scheme, the frame time is equally divided among the active cells and

the time share for each ce11 is again equally divided among the active mobile terminals. In

the DTSA-B scheme, the frame time is equally divided among the active mobile terminals.

The time share for the mobile terminals using DTSA-A and DTSA-B schemes are shown

in Figure 4.5 (c) and (d) respectively. The timing diagram for each scheme is also shown

in Figure -4.5. Here. the DTS.4-A and the DTS.4-B are just two different exarnples in the

dyriamic scenarios.

I I FTSA 1 DTS-4-A 1 DTS.-\-a

1 to the minimum time share 1 1.0 1 2.0 1 1.0 1

L

Fraction of frame time utilized Ratio of the ma~imum time share

Table 1.2: Summary of the performance of the FTSA and DTSA schemes.

Table 4.2 summarizes the performance of the three schemes for the exarnple shown in

0.5

Figure 4.5 (a). The DTS-A-.A scheme ensures that the time share is not wasted as in the

1

1 .O

FTSA scheme. However? it does not maintain fairness. i.e.. the ratio of the maximum

tinie share to the minimum time share is higher than that for the DTS-4-B scheme. The

DTSA-B scheme maintains faimess arnong the users in addition to ensuring that the time

is no t wasted. Since there are only three cells in the example we easily found the time

coordination arnong transmissions for the users according to the dynamic schemes. In larger

cellular networks, finding the time coordination using DTSA schemes will be complicated.

4.4 Time Coordination Schemes

In this section we develop a dynamic tirne share allocation algorithm that can uniquely

find the fair fraction of the frame time for each active user in the downlink. Internet. users

would like to get as much time share as possible in downlink to reduce their downloading

time. However. the amount of time share that can be allocated depends on the user traffic

demand in the area. The goal of our time share allocation algorithm is to allocate as much

time share as possible for al1 the active users while maintaining fairness among the users.

Fairness is defined in Section 4.1.2.

The proposed tirne share allocation schenie has two phases. When a cal1 request arriws

to a ce11 site the cal1 admission policy should make a decision to accept or rejcct the call

in the first phase. The decision should satisfy a guaranteed minimum time share (Tmin).

that will subsequently provide a minimum rate requirement. in each frame. the lime share

for the users is found according to the nuniber of active users in each cell. CVe assume

the priority level for dl the users are the same. CVe will explain hoa the algorithm can

be modified to incorporate different prîority Ievels for different users in Section 4.5.4. We

will develop the call admission policy and the dynamic time share allocation algorithrn in

the next sections. Mie will also discuss a fixed t ime share allocation algorit hm t hat will be

used for corn parison.

4.4.1 Fixed Time Share Allocation (FTSA)

The FTSA scheme is a simple time share allocation scheme that allocates the frarne tirrie

equally among the cells without taking demand into consideration. The users rvill be

ailocated a fked amount of time share regardless of the network traffic.

The FTSA Call Admission Policy

In the FTSA scheme. the cdls will be blocked if the nurnber of users in a ce11 exceeds the

threshold iimit. The limit is determined by guaranteeing a minimum rate to al1 the uses

in the network. In the FTSA scheme. since the time reuse cluster size is three cells each

ce11 will get a tirne share of T/3 regardless of the user traffic demand. If the minimum rate

requirement for a mobile terminal is Rrq7 the corresponding time share beq = Rreq/R.

where R is the k e d rate provided in the system. Hence, the maximum number of users

(JInSA) that can be accepted in a ce11 using the FTSA scheme can be given by:

where 1x1 is the largest integer l e s than z. Therefore. c d blocking d l occur when there

are MFTs=\ users in the ceII. The FTSA scheme will block a call in a ce11 with .Cfms.4

users even tliough t here are l e s than .kIFTS.4 users in the adjacent celis. In a system wit h

three secton per cell. t his number will be approximately three times higher (Le.. JillFrsn).

because the time can be reused in the three sectors of each cell.

The FTSA Algorithm (FTSA-Alg)

The base station will transmit for a fixed amount of time to each user for the duration of

the call. The FTSA scheme will allocate a time share as follows regardless of the nurnber

of users in a cell:

Tl3 time share = , ,

In the FTSA scheme. only the guaranteed minimum time share is allocated for the users

even though the demand in the ce11 and the adjacent cells changes r i t h time. In the next

section: we develop a dynamic time share allocation scheme that can make use of the idle

time share within and outside the cell.

4.4.2 Dynamic Time share Allocation (DTSA)

The DTSA scheme is not straightfomard unlike the FTSA scheme. Only information that

we have is the user distribution in the network and the time share (Tm). that is to be

allocated for an accepted user .LIm! must be greater than the minimum guaranteed time

share (Tmin) That is.

And also. the time share allocated for al1 the users in any overlapping cluster should be less

than the frame tirne (T). By an overlapping cluster. Ive rnean that every possible cluster

pattern that covers the ce11 rnust be considered. That is.

T,,,<T, Vcliisterpatterns. rn E cluster

We have to allocate Tm for al1 the users in such a way that the abow two conditions are

satisfied while achieving fairness and maximizing network utilization.

In non-uniform user distribution, it is not possible to provide equal time share for al1

the users to achieve m ~ u i m u m network utilization. The mobile terminais in the heavily

concentrated areas will get smaller share of time. One of the most dificult aspects of

dynamic scheme is treating al1 users fairly when it is necessary to reduce the traffic Row in

the network due to congestion. Faimess can be defined in a nitrnber of different mys. W e

define fairness among users as follows: Any user is entitled to as much time share as any

other user. This notion of fairness leads to the idea of mavimizing the time share allocation

to the users in the highly densed area.

In the DTSA scheme, we have the flexibility of allocating a non-uniforrn time share

among cells within a cluster. That is, the cells with higher demand can be allocated with a

larger time share compared to the cells Nith lower demand within the same cluster. A cell.

which is surrounded by many lightly loaded cek . can d s o be allocated with a larger time

share. For the example discussed in Section 43.1' we had the flexibility of allocating time

share in the folrowing ways: T4 = T and TB = Tc = O or T4 = T/2 and TB = TC = T/4.

There are many other combinations of possible allocations. The allocation of time share in e j

ce11 B will affect the available time share in cells -4. C and D. It can be clearly seen that

the allocation of time share in one ce11 will affect the available time share in the cells that

are covered by al1 the overlapping clusters which also covers the cell of interest. Therefore.

we cannot simply start allocating the time share for any user or cell. When a mobile

is allocated with a time share. the available time share in the neighbor cells is already

affected and that in turn will affect the allocation in its neighbors. like n chain reaction.

The question is where do we start.

If we look at this problem carefully, we can see the similarity betweeri t his problem, and

the flow control problem studied in the broadband wireline n e t w d s a decade ago. Based

on the similarities we have modified a previous work on mal-min flow control [52,53]? to

propose a novel dynamic time share allocation algorithm that can uniqüely find the fair

time share for the users in cellular networks.

The DTSA C d Admission PoIicy

A cal1 will be blocked in the DTSA scheme if the number of users in any of the possible

cluster patterns that covers the ce11 of interest exceeds the threshold limit. The threshold

is determined to guarantee a minimum data rate. The maximum number of users (.\lmsS4)

that can be accepted in a time reuse cluster can be given by:

It can be noted that l\.lms,i = where .\Ims.4 and ~ \ ï ~ ~ ~ . ~ are the rnaurnum

number of users in a cell using the FTSA scheme and in a cluster using the DTSA scheme

respectively Therefore, the maximum auniber of users that can be accommodated in a

cellular network using the FTSA and DTSA schemes are the sarne. nhich ni11 occur when

al1 the tells are uniforrnly filled with i'C~FTS.4 users per ceIl.

The DTSA Algorithm (DTSA-Alg)

The DTSh-Alg is an iterative algorithm. It finds and allocates the time share incrementally

in each iteration. This algorithm can be used to find the time share allocation for both

sectored and non-sectored cellular systems. The users in the heavily loaded cells are the

most poorly treated users because they will get the lowest time share. To be fair. we want

to allocate the maximum possible time share to these users. The idea of the DTS.4-Alg is

to start with all-zero time share and to increase the share For users in al1 clusters together

until the sum of time share for al1 the users in one or more clusters reaches the frarne time.

Le., the cluster saturates. This algorithm guarantees to allocate the mavimum possible

time share to the most poorly treated users. After the most poorly treated usen are given

the greatest possible time share. there might be extra time share for choosing allocations

for the rest of the users. Tt is then reasonable to mavimize the allocation for the most

poorly treated users within the current set of users. This procedure is continued until al1

the possible allocations are made. This algorithm maximizes the allocation of each user

subject to the constraint that an incremental increase in user A's allocation does not cause

a decrease in some other users? allocation that is dready as small as -4's allocation or

smaller. CVe will illustrate the DTS.4-.Mg in an algorîthmic form in this section and will

show the details using an example in Appendk A.

List of parameters:

0 rn: mobile terminal. rn E {Ml, i\12, .., MLv}, AIx is number of terminals in the network

O 1: cluster patterns. 1 E { L i , L2? . . ,LM), Lw is number of possible cluster patterns

0 T: frame time

O k: iteration step

O Lk: set of cluster patterns not saturated at the beginning of k

0 .CI": set of mobile terminals not in any saturated clusters at the beginning of k

O nf : number of mobile terminals that are in cluster 1 and not in Alk

a tk: time share increment added to al1 the mobile terminals in J I k a t iteration k

0 T:: accumulated time share for mobile terminal rn at k

O Tt: total allocation of time share in cluster 1 at k

The Aleorithm:

1. Find the maximum possible incremental time share: pp-'

tk = rninlELt +, where nf = number of terminals rn E J I k in cluster 1 " 1

2. Allocate incremental time share:

TA-l + t k , for nt E Mk

Tk- O t henvise \

3. Identify the clusters that are not saturated and the mobile terminals that are not

belonging to saturated clusters:

Lk-1 l = { 1 1 T > q k } , nrhere qk = &l Th

AIk+' = { m / rn not in 1 $ Lk+l}

4. Stop or continue:

if Jfk = {} then stop. else k = k + 1. go to Step 1.

Figure 4.6: A n example Nith 7 cells and 11 mobile terminds: 6 different cluster patterns.

Let us consider an example shown in Figure 4.6(a). There are 11 mobile terminals

(:\il - Adll) in 7 cells (Co - Cs) with unequal user distribution. There are six different

cluster patterns ( L I - L6). Three of them are shown in Figures 4.6 (a), (b) and (c). ;\I1 of

them overlap at the center cell.

First. the most heavily concentrated cluster (L4) is identified to find the incremental

time share. There are six mobile terminals in it. The mobile terminals in this cluster will

get the minimum share of time. Therefore, we want to allocate the maximum possible

time share for them. The time share is the frame time divided by the number of mobile

terminals in this cluster (T/6). At this iteration. the time share T/6 is allocated for all t h e

mobile terminals in the network. Hence. cluster L4 saturates a t this point and the mobile

terminals in the rest of the clusters can be allocated additional time share. The time share

allocation in the first iteration is shown in Figure 4.7(a).

(a) First itention (b) Secod i tmtion tc) Third itcntion

Figure 4.7: Incremental time share allocation in three iterations.

In the next iteration, the mobile terminals in all the clusters except L4 can get more

time share increment. However. all these five clusters (L I , L2, L3 , Ls. Ls) cover mobile

terminal .\.II which has already been allocated the maximum possible share (T/6). Hence.

it is important to distinguish the mobile terminals that are covered by already saturated

clusters from other mobile terminals. The mobile terminals that are not covered by any

already saturated cluster can get additional time share in the next iterations. To identify

the clusters that are not saturated: the accumulated time share for each cluster is found

and compared with the frame time. If it is less than the frame time the cluster is not

saturated. A11 the clusters except L4 are not saturated yet. In these five unsaturated

83

clusters mobile terminals ,\.II - :LI5, which are in L4? are eliminated for furt her time share

increments.

In the second iteration. cluster L5 has the lowest ratio of available time share (T -

5 * T/6 = T / 6 ) t o the number of mobile terminals (:LI7, il&) that do not belong to L4.

Hence the increniental time share is (T/6)/2 = T/12. hl1 the mobile terminals that do not

belong to L4 will get this additional time share. Figure 4 3 b ) shows the allocation of tirne

share in the second iteration. At this point mobile terminals JI1 - -\& have been allocated

the maximum possible time share. In the third iteration the rest of the mobile terniinals

(1Vf9 - Mil) wil1 get their maximum possible time share. Figure -144 shows the allocation

of time share in the third iteration. From Figure 4.7, it can be seen that ive cannot increase

the time share o f a mobile terminal without decreasing the share of other mobile terrninals.

whose time share is already less t han its share. Hence. the allocation is fair. Figure 4.8

illustrates the transmission time for each mobile terminal and the noordination among

the neighboring cells. The transmission tirne of base stations that are within the tirne

reuse distance do not overlap. For this example. each step of the DTSA-Alg for the three

iterations are shown in Appendix .A.

Figure 4.8: Time coordination m o n g neighboring cells.

4.5 Simulation Results

We consider a cellular network of N x N hexagonal ce11 g id s with varioiis network sizes

as small as 100 colls (N=10) and as high as 10,000 cells (N=100). Each ce11 has three

sectors as shown in Figure 4.4, hence. there are 3iV2 number of sectors in the cellular

network. The mobile terminals are non-uniformly distributed in the network. The number

of mobile terminals in each sector has a uniform distribution between O and 10. We find

the Fair fraction of the frarne time that should be allocated for each active mobile terminal

to receive data.

4.5.1 Time Reuse Efficiency

IO t 5 lteration number

Figure 4.9: Time reuse efficiency of FTSA and DTSA algonthms.

For a cellular network with a nurnber of sectors in total and a tirne reuse cluster size of

b cells, the maximum of the sum of time share that c m be allocated in terms of frame time

(T) c m be written as aT/b. The time reuse efficiency is defined as the ratio of the sum

85

of the time share allocated for ail the mobile terminals in the network to the maximum

amount of frame time that can be in reuse. That is,

~2 Ti time reuse efficiency =

aT/b '

where. Tm is the tirne shaw allocated for mobile terminal Mm and .II\. is the number

of mobile terminals in the entire network. Figure 4.9 shows tirne reuse efficiency for the

FTSh-Ag and the DTSA-Alg as a function of the nurnber of iterations. These results were

produced for a network of 100 cells (300 sectors). The average value of time reuse efficiency

is produced by running the algorithm with various user distributions. Since the DTS-LAlg

incrementally allocates the t ime share in a few iterat ions, the time reuse efficiency increases

with the number of iterations until it converges. Hoivever. the FTSA-Alg allocates the time

share in just one step. hence, the efficiency does not change with the number of iterations.

The DTSA-Alg performs much better than the FTS.4-Ag in terms of time reuse efficiency.

Since the users are non-uniformly distributed in the network. neither of the algorithm have

an efficiency of one. For example, if al1 the mobile terminals are concentrated in one half

of the network and the other half is empty, the efficiency will be obviously less than 0.5

for either algorithrns. bloreover, if the mobile terminals are uniforrnly distributed in the

network, i.e.. the number of mobile terminals in each ce11 arc the same. then the tirne

reuse efficiency for both the FTS.4-Alg and DTSX-Alg will be one. Hoivever. we consider

nokunifonn distribution of usefs in the -stem which will keep the effitiency less t han one.

In the FTSA-.Mg. the time share available for a sector is T l 3 as it can be seen in

Figure -L.-l(a). The average number of mobile terminals in a sector is 5 because it varies

uniformiy between O and 10. Since we assume that the network admits up to 10 users in

a sector, in the FTSA-Alg, each mobile terminal is given T/30 even though there are only

5 mobile terminals on average in a sector. If there are exactly 5 mobile terminals in each

sector, the FTSA-Alg could have allocated T/l5 per user which could have led to a time

reuse efficiency of one. However. according to the varying user distribution. the FTSA- Alg

will have an efficiency of 0.5 on average.

in the case 0fDTS-t-AIg. it starts aiïocating time share by considenng the most heaviIy

loaded clusters. If there was a cluster with d l three sectors having 10 mobile terminals

each, which could be the most heavily loaded cluster. the time reuse efficiency a t the first

iteration could be 0.5. Since it is a rare case, the time reuse efficiency at the end of the first

iteration will be slightly higher than 0.5. Here, the first iteration results are better than

the time reuse efficiency of the FTSA-Alg. In a cellular network with non-uniform user

distribution, the DTS.4-Alg has a 50% better time reuse efficiency than the FTS .A-Mg.

4.5.2 Range of Time Share Allocations

1 Average minimum time share 1 0.033 T 1 0.039 T 1

1 to minimum time share ( 1.0 1 1.7 1

Average maximum time share Average ratio of masirnum

Table 4.3: The range of time share that nrill be allocated by the FTS.4-.Mg and DTSA-Ag.

Table 4.3 shows the range of the tirne share that will be allocated for the mobile

terminals in a network of size 100 cells with each sector having a number of mobile terminals

that =ries between O and 10. According t o (4.7). the FTS.4-.Mg will allocate a tirne share

of 0.033 T to the users regardless of the user distribution. Homever. the DTSA- Alg allocates

as hi& as 0.066 T and as low as 0.039 T according to the user distribution. The number

of mobile terminals per sector varies from O to 10. However. the ratio of the maximum

tirne share to the minimum time share is o d y 1.7 in the case of DTSA-Alg. That is. the

mobile terminals will get a fair amount of time share regardless of the user distribution in

the network.

To achieve a higher time reuse efficiency the FTS-.-.Mg can be modified as follows:

0.033 T 0.066 T

Table 4.4: Algorit hm execution tirne for different sizes of cellular networks.

After allocating the frarne time equally among the sectors. time share can be allocated for

mobile terminals on demand instead of a fixed share. If al1 the sectors are operated Mth

the mavimum capacity, the time reuse efficiency can reach one. However. the minimum

time share that will be allocated in the netrork will still be 0.033 T. When there is only

one mobile terminal in a sector. the mobile terminal will get a tiiiie share which is one-

third of the frame time, equivalently. ten times higher than the minimum time share. This

amount of time share may be high for a single user. The ratio of the maximum time share

to the minimum time share allocated in the network can be as high as the number of users

that are allowed in a sector. Therefore. the DTSA-Alg that we proposed is relatively fair

and efficient in allocating the time share to the mobile tenninals in a cellular network nith

non-uniform user distribution compared to the FTSA-Alg.

Network ' Size ( c e k )

4.5.3 Algorithm Execution Time

Execution time (ms)

The algorithm execution times are s h o w in Table 4.4. The table also shows the number

of iterations the algorithm takes to converge. This is a centralized algorithm. The central

processor of a reasouably large cellular network size of 100 cells (300 sectors) takes about

12 ms (18 iterations) on an Ultra 30 Sun workstation. The number of iterations are found

to be much less than (in the ivorst case, as high as in the order of) the number of cells in

the network. These results show that the algorithm can be implemented in real-time.

Number of ' sectom

' Number of

' users/sector Number of

' iterationç

4.5.4 Enhancements to the DTSA-Alg

The DTS-\-Mg can be modified within the same framework to allocate the time share in

various scenarios. The modified algorithm can improve the performance. Following are a

fem examples of such situations:

1. Different users can be given different priority levels represented by integer numbers.

The larger the number the higher the priority The algorithm should be modifieci to

encounter the weighted sum of the users when counting the number of users that do

not belong to a- saturated cluster. Hence. the incremental time share will represent

the share that should be allocated to the user with priority level one. The other users

will get a share proportional to their priority levels.

2. Different minimum time share (Tmin) guarantee c m be provided for different mobile

terminais. This is neccssary hecaiise not al1 the users want t he best service. Some

users ma? want lorv quality service (Le., low T,,) for low price. whereas some other

users want the best service for an>- price. This can be done at the cal1 admission

phase for each c d .

3. Different maximum time share (Tmax) can be provided. Some users do not need

more than Tmax. In this case. the users with the maximum time share constraint

should be provided with Tm= and the excess frame time share for al1 the users with

the constraint shonki be te-altocated fair- among the users r i t h no mch constraint.

4. bfany classes of services with different values of T& and Tmax pairs can be provided

within the same framcwork.

4.6 Chapter Summary

We considered a cellular system with tirne reuse cluster size larger t han one ce11 t hat

minimizes the interference and elirninates the problem for users in the ce11 boundary. TDhI

mode transmission \vas corisidered. The users are allocated fked rates by varying the

transmit power. Each user wiIi be receiving data for a fair fraction of the lrame time

according to the user traffic demand. When a user receives data. the other users wit hin

the time reuse distance cannot receive.

The proposed dynamic time share allocation algorit hm (DTS-4- Alg) uniquely finds

the fair time share for each user to receive data from the base station for a given user

distribution in a cellular network. W e have demonstrated the effectiveness of the DTSA-

Xlg in terms of time reuse efficiency and compared it w i t h the fixed time share allocation

algorit hm (FTS.4-Mg) t hrough simufat ion. The t ime reuse efficiency of the DTS.4-.Mg

is about 50% better than that of the FTSA-Alg, on average. Moreover. the DTS-4-Alg

maintains fairness among the users. That is. the ratio of the maximum tinie share to

the minimum time share allocated in the network is in the same order. The proposed

DTSA-Mg is fa i r and efficient in allocating time share in non-uniform user distribution.

However, the DTSA-Alg needs more computing power than the FTS.4-Ag. The DTS.4-

Aig is an iterative algorithm. W e shorved by simulation that the DTSA-Alg converges

reasonably faster for a relatimly larger cellular network with few hundred cells. Therefore.

the DTS-A-AIg can be implemented in real-time.

4.7 Appendix A: A Numerical Example

fn this section, we miIf illustrate the dynamic time share allocation aigorithm (DTS.4-Mg)

in detail with an example. For a better understanding of the algorithm, we will show al1

the steps and the status of the resources at the end of each iteration step.

Let u s re-cal1 the exampie given in Section 4.4.2. Here. the tirne reuse cluster size is

t hree cells. The frime time T is assurned to be 36 ms, We ntill find the fair share of time

for al1 the mobile terminais next.

Execution of the Algorithm:

ta) User distribution ibb Fair time shvc in mec

Figure 4.10: An example with 7 cells: (a) 11 mobile terminals (b) fair time share in ms.

1 Start 1 qo = O. Ta& = O, k = 1. .\Il = (.\Il. JI2. ... JIl 11, L1 = { L I . L2? ... L6) [Initial vaiues]

1 1 = 6, nLs = 5, n,, = 4

36-0 3G4-0) = 6 5 '

-At the end of the first iteration. al1 the users are given a tirne share of 6 rns. The cluster

LI is saturated because it reached the €rame tirne? 36 ms. The users in t his cluster namelu'

.\il - .\f6, received their greatest possible time share and they will not get any increment

in the next iterations.

At the end of the second iteration. al1 the users who are not in the ciuster L4 received

an increment of 3 ms. This increase in the time share made the cluster L5 saturate. As a

result. the users in cluster L5 namely. MT and 1\&. will not get any more incrernent in the

next iterations.

lTZ-1

3. T:, = 36. T;., = 26. ~2~ = 22. ~2~ = 36: T:, = 36. Tt6 = 34 [L, saturated]

Li = { L * . L3. L s }

LW= { }

At this iteration step. al1 the users who are not in clusters L4 and L5 received a maximum

possible increment of 1 ms. This incrernent makes the cluster L to saturate. Now. Ive can

see that there are no more users Ieft who are not in the already saturated clusters L4. L5

and Li. This leads to an end of the execution of the algorithm. Figure -L.lO(b) shonps the

fair share of time in ms.

Chapter 5

Time Slot Allocation Schemes

In the last chapter. a fair time share allocation algorithm ivas proposed to coordinate the

transmissions among base stations in a cellular network with tirne reuse c!uster size larger

than one cell. At the beginning of a frame the active users request for data. The users are

scheduled to receive data in different segments of the frame tirne according to the demand.

The arnount of data that can be received by the users varies with time as the demand

changes. The algorithm should be run for every frame and is useful for best-effort services.

Introduction

In t his chapter. we consider real-time applications such as video telephony. These applica-

tions reguire a constant bit rate service. We consider TDM mode transmission to provide

high data rate services. The nurnber of real-time connections that can be provided for the

high data rate senrices are smailer compared to the low data rate connections. In providing

real-time services, it is necessary to guarantee a tived arnount of time share in each frame

so that a constant average bit rate can be rnaintained over each frame. .A guaranteed tirne

share has to be allocated for the entire duration of the connection. Since the nurnber of

connections are smdl and a segment of time share is r e s e ~ e d for the entire duration of the

connection. maqv new calls will be blocked. In providing real-time high data rate semkes

cal1 blocking is a major issue. In this chapter we propose a time slot allocation scheme for

real-tirne Bigh data rate applications.

The rest of this chapter is organized as follows: The system niodel is described in Sec-

tion 5.2. The conventional channel allocation schernes are discussed in Section 5.3. The

unified channel allocation scheme and the virtuai channel set are explained in Section 5.4.

The performance resuits and the implernentation issues are discussed in Section 5.5. Fi-

nally, the chapter summary is given in Section 5.6.

5.2 System Mode1

We consider a cellular system mode1 that is similar to the one given in Section 4.2. The

system has sectored cells. However, ive will discuss the channel allccation schemes with

a non-sectored ceIlular system for simplicity, and will show the simulation results for a

cellular system that has three sectors per cell. CVe coasider a time reuse cluster size of

three cells and TDM mode transmission. Figure 5.1 shows the time-slotted structure for

one of the proposais for the third generation wireless systems. There are 15 time slots in

each frame [la]. The length of the frame is 10 ms. It may not be necessary to reserve a

time slot in each frame, but in every few frames. This depends on the average data rate

of the application and the data rate that can be provided by the system.

15 time dots h e

.....- # < I I

.*.*. ' I I / I ~ l i I I / l i l IÏTW 1 I i ] l / i i i ; j i I I ~ I , ; ...... - ' i ] i i i i : u I i ! i j / j ! T ! , l .4.-.-.

Frame F Frarne F+ 1 Frarne F+2 :- - - w:4 w:4 -:

(10 rns) (10 ms) (10 ms)

Figure 5.1: Time-slotted structure with time dots and frames.

Let u s assume it is necessary to resewe a time slot for every two frames. Le.. for every

20 ms. There are 30 time slots in 20 ms. We cal1 these time slots as channels throughout of

this chapter. These 30 channels can be used within any possible cluster pattern. In fixed

tirne sIot docation schemes. there wilI be 10 (Le.. 30 channeIs per cluster of three cells)

channels available for every cell. In the sectored cellular system these 10 channels can be

reused in al1 the sectors of a cell. Hence. up to 30 users can be admitted in a ce11 for a

system with three sectors per cell.

5.3 Channel Allocation Schemes

A channel allocation scheme decides what channel is to be assigned when a cal1 request

arrives at the base station. The choice of the channel allocation scheme should cope with

the time and spatial variations of the traffic demands in cellular networks to avoid too

maay cal1 blockings. The currently known channel allocation schemes can be divided into

fixed, dynamic and hybrid schemes.

In the k e d scheme, there is a constant number of channels pre-assigneci to a cell. The

same channels are used in the cells t hat are located a t sufficient distances apart satisfying

the reuse distance criterion [-LI. If a channel is used in one cell. the channel cannot be

simultaneously used in the cells that are nithin the reuse distance. In k e d schemes. as

soon as al1 the assigned channels in a ceil are used up. no more traffic in that ce11 can

be served even though there may be idle channels in the neighboring cells. The major

deficiency of the fixed scheme is that it cannot accommodate spatial and temporal traffic

variations efficiently. Manu channei borrowing schemes [54-561 were proposed Iater

relaving the channel assignment rule in the basic fked scheme. In these schemes a channel

can be borrowed from the neighboring cells. if it is idle and the reuse criterion is still

sat isfied .

In the dynamic schemes, al1 the channels are placed in a pool. and are assigned to nea

calls on a needed basis such that the reuse criterion is satisfied. That is. any channel can be

used in any ce11 unlike in the fked schemes. In the dynamic schernes. the oniy constraint is

that. if a channel is to be used in a cell. that channel should not be used by t.he neighboring

cells within the reuse distance. Hence. a call request in a cell will not be blocked as long as

the ceII has an idTe channe1 mit hin the neighborhood of t l e ceII. Previous st udies [JI. J7.58/

show that the dynamic schemes perform better than the fked schemes iinder Ion traffic

intensity. However. the fked schemes become superior at higher offered traffic. especially

in the case of uniforrn traffic. In the case of non-uniform traffic with light to rnoderate

loads, the dynamic schemes perform better due to the fact that under low traffic intensity,

the dynamic schemes use channels more efficiently than the fked schemes.

The hybrid schemes [59-611 are a mixture of the fived and the dynarnic schemes where

the available channeIs are divided into f i ed and dynarnic channel sets. When a cal1 arrives

in a cell, it will first t~ to assign a channel from the fixed channel set. If there is no

channel available in the k e d channel set of the cell, a channd from the dynarnic set mi11

be allocated. The performance of the hybrid schemes depends on the ratio of the fixeci to

dynamic channels. Since it is a combination of the h e d and dynamic schenies. the hybrid

schemes have the advantages of both schemes. In uniform traffic situations. call requests

will be evenly distributed over tke network. Hence, the channels in the fixed channel set

will be useful to reduce call blocking. However, as the traffic intensity increases. the fixed

channel set wïll become empty. .As a result. a channel from the dynamic channel set will

have to be used even though the traffic is uniform. Using the dynarnic schemes in uniform

trafEc environment is not very efficient [dl] especially in heaw traffic. Therefore. having the

dynamic channel set in uniform traffic environment nlll become a bottleneck in the hybrid

schemes. Similarly having the fixed channel set in non-uniform traffic environment is also

a disadvantage in the hybrid schemes especially in light traffic. Therefore. in the hybrid

schemes, hating two channel sets is beneficial in some trallic environment while becoming a

bottleneck in some other traffic environment. Another proposa1 in (411 suggests to use the

fived schemes in heaw tr&c conditions and. the dynamic schemes in low tr&c conditions.

One problem here is the transition from one scheme to the other when the traffic condition

changes. This transition may cause an increase in c d blocking. Furthermore. the system

wili realize that it needs to switch to the other scheme only after blocking a certain number

of calls. Hence, this scheme is not a good choice if the t r 6 c situation changes quite often.

In this chapter, we introduce a scheme caired vniEed Channel -4lbcation (CC-4) scheme

which is shown to perform better in al1 trafic conditions.

5.4 Unified Channel Allocation (UCA) Scheme

In the UCA scheme. when a call request arrives in a cello the base station controller will

assign a channel from its virtual channel set (VCS). The VCS for a ce11 contains the channels

which are recommended to be used in that ce11 in order to increase the system capacity

by efficiently packing the channels. .As a result. future call blocking can be reduced. The

channel pattern in the VCS can lead to a maximum packing nf users in the netrvorkL. If al1

the channels in the L'CS are busy it will choose any other free cha~nel t hat is not being used

in the neighborhood of the celi. In the LTCA scheme, no channel can be idle because the

VCS does not resente any channel. but it just recommends the channels to be used in the

ceii. The VCS of a ce11 ni11 force its channels, that are borrowed by the neighboring cells

to return to its original cel12. Therefore. the channels d l not move al1 over the network

as in the dynamic schemes.

The UCA scheme looks like the fixed schemes because it attempts to assign a channel

from a set of channels associated with the cell. On the other hand. it also Iooks Iike the

dynamic schemes because it chooses any other free channel if the first attempt failed. The

U C A scheme, homever- is neither a kxect scheme nor a àynarnic scheme. In fact. the CC-4

scheme unifies the advantages of the Lxed and the dynamic schemes. Hence. we call it as

the UniJied Channel Allocation scherne.

'Maximum packing of channels can be achieved by reusing the channels in the minimum possible distances wit hout violating the reuse criterion.

'In the UCX scheme. uniilce in the dynamic schernes the channels wiIi corne back to the original ce11 even though it is aüowed to move around the network since the VCS keeps the recommended channels.

5.4.1 Virtual Channel Set (VCS)

The VCS of a ce11 consists of a set of recommended channels that c m Ieâd to the maximum

packing of users in the network. It differs from the k e d schemes in t hat the cells are not

restricted to use the pre-assigned channels, but any ce11 can use an- channel as in the

dynamic schemes. Therefore, ive cal1 this channel set a virtual channel set (VCS).

If the traffic condition is not varying with time. assigning a channel from the VCS will

be successful most of the time. In time varying traffic conditions. it may not be always

possible to assign a channel from the VCS. -1s a result, a channel which is not recommendcd

to be used in a particuiar ce11 will be assigned so that the call is not blocked.

In the VCS, we do not reserve any channel for a ce11 as in the fixed schemes which are

reservation schemes. Therefore. the channels in VCS will not be found idle as in the fixed

schemes. Next. we will discuss three different types of VCSs appropriate for various traffic

conditions.

Uniform VCS

In this case. an equal number of channels can be found in the channel set for each cell.

Hence, we call this uniform VCS. This C'Cs is most suitable in uniform traffic conditions.

The attempt to find a channel from the VCS will be successful most of the time because

in uniform traffic conditions. approxirnately the same number of cal1 requests will arrive

in each cell. The CCA scheme with the uniform \;CS does not need additional cornputing

power and it is simple to implement. However. it wil1 not perforrn well in non-uniform and

time varying t raffic conditions.

Non-Uniform VCS

This VCS is most suitable in a non-uniform tranic environment where permanent hot-

spots exist. An example of permanent hot-spot is the apartment buildings. where the

call request demand will be much higher. These hot-spots need more channels than the

rest of the area. If we use a uniform VCS in an area mith hot spots. there will be many

unsuccessful attempts to assign channels from the VCS in the hot-spots. If an attempt

is unsuccessful. a channel. which is not in the VCS of that cell. will be assigned to the

call. This channel is not the best channel to be used in this ce11 for efficient packing,

but the call can be accepted instead of blocked. Usage of this channel. hoivever. can lead

to future call blocking. There are two potential techniques for further reducing the call

blocking probability. The first one is that if the attempt is unsuccessful. find the niost

appropriate channel to be used in that ce11 rather than just assigning an arbitrary idle

channel. This will be complex because it needs the mesure of future call blockings. as

well as some predictions of the future traffic. The second solution is to keep more channels

for the \ C S of the permanent hot-spot cells. The number of channels to be kept for the

VCS of the hot-spot cells and the rest of the area should be decided according to the user

demand in the residential area. Since the VCSs in an area with some hot-spots will have a

different nurnber of channels to satisfy the demand we cal1 it as non-trnzfom VCSs. This

non-uniform VCS will reduce the unsuccessful attempts to assign a channel from the VCS.

As a result? cal1 blocking can be further reduced in a non-uniform traffic environment with

permanent hot spots.

Diynamic VCS

The traffic demand changes with time in red situations. For example. in the rniddle of a

day: we may find a uniform traffic distribution with high load intensity al1 over the city.

and in the middle of the night, the traffic may be uniform with low load intensity. On the

other hand, in the beginning and the end of a day, i.e.. in the peak hours. we may End

non-uniform traffic distribution. The uses may be concentrated on highways and roads.

Therefore. to satis- the demand due to the tirne and spatial variations of traffic. the W S

of each ce11 should also change with tirne.

This channel set is most suitable for dynamicaiiy changing traffic with respect to time

and space. The hot-spots are not permanent, but they move around in the area. In

this case. the VCS should be adaptively modified using past statistics in order to reduce

the unsuccessful attempts to assign a channel. If the \'CS does not cope nith timel-

and spatialTy varying dernand, the attempt to assign a channe1 from the K S wiII be

unsuccessful. As a result. future call blocking MI1 be increased.

5.5 Performance Analysis

In t his section, we will study the call blocking probability of the UC.1 scheme by comparing

the performance of the basic fiuedldynamic schemes. The performance of the hybrid

schemes varies between the fixed and the dynamic schemes. This depends on the ratio of

the number of the channels in the fked and the dynamic sets. We consider both uniform

and non-uniform traffic situations to demonstrate that the ÜCA scherne can perform better

than the other channel allocation schemes in any traffic environment. L k d l first discuss

the simulation parameters. and then show the results for uniform and non-uniform trafic

environment. The results are produced using ewnt-driven siniulation.

5.5.1 Simulation Paramet ers

The hexagonal cells in the system are organized as an X.uX array. The simulation results

are produced for a cellular network of size 100 cells where each ce11 has three sectors.

1 'iumber of cells 1 100 1 Number of sectors

1 Mean arriva1 time 1 1 /X 1

Table 5- 1 : Cornmon simulation parameters.

The common parameters for the simulation results are summarized in Table 5.1. There

are 30 separate channels in the system. W e consider a time reuse cluster size of three cells in

Our simulation. Therefore. in L~ed resource allocation schemes. t here will be 10 (Le.. 3013)

channels available per sector. Hence, each sector can accommodate as rnany as 10 users.

Since the network size is 100, the maximum number of users that c m sirnultaneously exist

and receive services in the system is 3000 (Le.. TO x 100 x 33 tising any channei atlocation

scheme. We did not consider mobility in Our study. Therefore. we did not measure the

hand-over blocking probability. Call duration is exponentially distribu ted wit h mean 1/p

(=IBO sec). Call arrivai is modeled as Poisson process nith 1/X calls per sec. Al1 Our

simulations are carried out for the duration of one million call arrivals.

5.5 .2 Implementation of Channel Assignment Schemes

Fixed Channel Assignment (FCA)

This section explains the channel assignrnent schemes t hat are simiilat cd for corn parison.

When a caI1 arrives to a sector. the channel occupancy table for the corresponding sector

is checked to see whether there is any free channel available. Table 5.2 shows an example

of the channel occupancy table for the fixed scheme where x represents the channel in use.

If a channel is available. the call can be accepted and t ben the table shoiild be updated.

Channel N x 1/ Table 5.2: Channel occupancy table for the FC.1 scheme.

Dynamic Channel Assignment (DCA)

In ihis scheme. the kuowledge about the channei usage in the neighborhood is necessary

in order to acceptlreject a call and assign a channel. Figure 5.2 describes a distributed

dynarnic channel assignment scheme [58] which was originally proposed for non-sectored

cellular system. The DCA scheme uses a channel occupancy table for each sector. For the

example in Figure 5.2. there are 6 channels {l, 2,3,1,5.6) in the system. In the channel

Occupancy iablc for xctot Oa

-- 1

Figure 5.2: (a) Sectored-cellular systern, (b) channel occupancy table for a sector using the DCA scheme.

occupancy table for a sector. the number of columns is equal to the nümber of channels

in the system. and the nurnber of rom is equal to the number of interfering sectors. Each

sector will have a table to keep the channel usage information of its neighbors and itself.

The first row contains channel usage information about itself. Since this table has the

channel usage information about a11 its interferers. a base station can simply assign a

channel for a mobile terminal in its sector, simply by reading the table. In the DCA

scheme that ive consider, an arbi trary free channel from its channel occupancy table will

be assigned when a call arrives to a sector. For the example in the figure, if a call request

arrives to sector la. it can be accepted since there is a free channel in the neighborhood of

sector la.

Unified Channel Assignment

In this scheme? when a call arrives to a sectort it will first attempt to assign a channel from

its K S which will be similar to the one in Table 5.2. If the first attempt is not successN

then it will use its channel occupancy table, which mil1 be sirnilar to the one in Figure 5.2.

to assign a channel.

5.5.3 Simulation Results

In this section. we compare the cal1 blocking performance of the UC.4 scheme using the

uniform VCS. with the fixedldynamic schemes under uniform and non-uniform traffic

environment. There are various fived and dynamic channel allocation schemes discussed in

the literature and summarized in [JI]. We consider the basic k e d and the basic dynamic

schemes that ive described in the previous section for cornparison. That is. we do not

consider the fixed schemes with channel borrowing [54-561 and the dynamic schemes wit h

learning process [62,63].

Blocking Probability in Uniform Trafic Environment

In the uniform traffic case, al1 the cells in the network have the satne call request arriva1

rate. Figure 5.3 shows the cd1 blocking probability with increasing traffic load for uniforrn

traffic. Since the blocking probability is O for wry low load intensit- it is not s h o m in the

gaph . From the figure, ive can see that the fixed scheme has Iower call blocking probability

than the dynamic scheme. However, the UC.1 scheme is superior to the other schemes.

except that the fixed scheme performs slightly better for an extremely high load. This can

easily be explained as foIlows.

Since there edsts randomness in call arrivals, even though the traffic is uniform, there

can be a srnall difference in the number of calls arrived in each cell. Therefore, under very

high load intensity. the UC.4 scheme may Bnd an ernpty VCS. .As a result. it niay find a few

unsuccessful attempts to select a channel from the VCS. In that situation, it rnay asign

any other channel as the dynamic scheme would do. This will reduce the channel packing

efficiency. Hence, the fked scheme can perform slightly better than the UCA scheme in

a heavy uniform traffic load. However. the fked scheme does not perform better than

the UCA scheme by a significant amount. even in the extremely high load that we are

interested in. Therefore, we c m conclude that the UCA scheme c m perform equally well

or better than the other schemes in a uniform trafic entironment.

15 18 2 1 24 27 Traffic Load (Erlmg/cell)

Figure 5.3: Blocking probability for uniform traffic; the network has 100 sectored cells and 30 channels.

Blocking Probability in Non-Uniform Tkafnc Environment

To simulate the performance of the UC.4 scheme under a non-uniform cal1 arrivai distri-

bution, we

distributed

ce1ls.

Figures

assume 10% of the cells are heavily loaded. Heavily loaded cells are evenly

over the region. and have cal1 amial rates x% higher thaii the lightly loaded

5.4 and 5.5 show the cali blocking probabilities for non-uniform traffic with x

values of 10 and 30 respectively CVe can see that x = 10 and 30 correspond to a slightly

non-uniform traffic and an extremely non-uniform traffic respectively In fact . r can take

higher values. however. for demonstration purposes x =

uniformity

For the slight Iy non-riniform t r&co the performance

the performance shown for the uniform t r a c . The LiCA

30 git-es a reasonably good non-

of al1 the schemes are similar to

scheme behaves like the dynamic

Figure 5.4: Blocking probability for slightly (z= 10) non-uniform traffic: the network has 100 sectored cells and 30 channels.

scheme in iow load. and the fixed scheme in high load. However, the UCA scheme performs

better than the other two schemes for any load intensity.

In extremely non-uniform traffic, as ive mentioned earlier, 10 out of 100 cells in the

network are hot-spots. These 10 cells have call request amval rates 30% higher than

the other 90 c e k In the uniform trafic case. eaeh of the 200 cells will receive about

10,000 cal1 request arrivais on average. out of the 1 million cal1 arriials to the system.

But. in the extrernely non-uniform traffic. the hot-spot cells wiil receive about 12.600 call

requests per ceil and the other 90 cells will receive about 9.700 cal1 requests per ce11 on

average. Therefore, in the n'ced scheme, a significant amount of the call requests to the hot-

spots m d l find an ernpty channel set while there are several free channels available in the

neighborhood. In the hot-spots, the dynamic scheme can assign more channels than would

be possible in the fixed scheme by using the fact that the neighbor cells have fewer number

1

le- l

6 9 12 15 18 2 1 24 27 30 Tnffic Load ( Erlang/ccltl

Figure 5.5: Blocking probability for ext remely (t=30) non-uniforrn t raffic: the network has 100 sectored cells and 30 channels.

of users. Therefore, the d-ynarnic scheme is superior to the fixed scherne in the evtremely

non-unifom t raffic situations. However, the UCA scheme performs slightly bet ter t han the

dynamic scheme for any load intensity. since the UC.4 scheme uses the VCS to pack the

channels more efficiently. Hence. we can conclude that the UCA is superior to the other

schemes at any load intensity for non-nniform trafic as ml.

Table 5.3 shows the traffic load in terms of Erlang/cell for a cal1 blocking probability

of 0.01. The trafic load can be increased in sectored cellular system approximately by the

number of sectors. As the traffic pattern becornes more non-linear, the traffic load that can

be carried decreases for a11 three schemes. bloreover. the trafic load t hat can be carried

decreases sharply in the case of the FCA scheme. In the case of DCA and C:CA schernes.

the traffic that can be carried becomes equal. The percentage increase in the traffic load

using UCA scherne conipared to FCA and DCA schemes shows the time reuse efficiency of

the UCA scheme.

Traffic load ncrease in C'C'A

Table 5.3: Traffic Ioad comparison for cal1 blocking probability 0.01.

Cniform t raffic Non-unifonn traffic (r=10)

In the simulation. we have implemented only the uniform VCS with the CC.4 scheme.

However, we have explained that the UCA scheme can perform even better with non-

uniform and dynamic VCSs under highly varying traffic demand -cith tirne and space.

5.5.4 Implementation Issues

In the UCA scheme. each sector needs only the information about its interfrring neighbors

to assign a channel for a d l . Hence, the UCA scheme can be implemented in either

a centralized or distributed manner. Therefore, the UCA scheme is a practical channel

allocation scheme. The UC.1 scheme with the dynarnic VCS can be implemented in any

celIular network without the knowledge of the fluctuations in the traffic with regards to

time and space. because the VCS periodically re-configures itself to satisf- the varying

trafic demand. The LTCA scheme perforrns as ive11 as the k e d scheme if the traffic in that

area is uniform. and as well as the dynamic scheme if the traffic condition is non-uniform.

In the UCA scherne, we do not attempt to make major changes to the system. We do

not divide the channel set into two sets as in the hybrid schemes. The system does not have

to switch from one assignment scheme to another. CClat the UCA scheme does differently

than the other schemes is that it asigns channels in a more intelligent way using the VCS

when cal1 requests arrive. Therefore, this scheme does not introduce any additional issues

and constraints: but provides performance improvements.

Erlang/cell compared to

UCA 25.0 21.5 15.0

FCA 23.0 14.5

Non-uniforrn traffic (x=30) 1 7.0

FCA 8.7% 48.3% 114.3%

DCA 20.0 185 14.0

DCA 25.0% 16.2% 7.1%

A fixed scheme with a non-uniform pre-assigned channel set will have lower call blocking

in the hot-spots t han the basic k e d scheme that ive considered. Xevertheless. the channeis

can be idle in such a scheme as well, because it is also a reservation scheme. However, in

the UC.4 scheme with the non-uniform VCS, the channels are not fixed to the sectors so

they \vil1 never be idle if an adjacent sector needs them. As a result. the CC.\ scheme with

non-uniform VCS will perform better than the improved version of the fixed scheme.

An adaptive-fixed scheme can adaptively do the channel planning for the sectors using

past stat istics and predictions. This adaptive-ked scheme will perform better t han the

basic scheme. However, the adaptive-Exed scheme also suffers from the fact that the

channels are fked to the sectors.

An initial channel assignment plan is necessary for the dynarnic VCSs. If the demand is

known in the area, the VCS can be formulated accordingl. However. for an nrea where the

demand is not known or changes frequently, a uniform VCS can be used in the initial stage.

.As time progresses. the VCS for each sector d l be updated using the previous statistics

of the sector and the neighboring sectors. The central processor can perform the updates.

The update process can be done periodically or when the call blocking probability reaches

the threshold.

We have discussed three types of VCSs. We can see that the uniform \*CS is a subset

of the non-uniform VCS? and the non-uniform VCS is a subset of the dynamic VCS.

The implementation of the uniform VCS is relatively simple. On the other hand, the

implementation of the d ~ a r n i c VCS will be very cornplex, but can perform much better

t han the O t hers in spat ially and t imely v a ~ n g t raffic environment.

5.6 Chapter Siimmary

In providing real-time senices it is necessary to guarantee a tived amount of tirne share in

each Frarne. Cal1 blocking is a major issue in real-time high data rate services. To reduce

the c d blocking, the time slot (channel) allocation scherne should take the traffic variation

in time and space into account. CVe have discussed the currently h o w n channel allocation

schernes and their performance under various traffic conditions. The avaiiahie schemes

are not appropriate to satisfy the needs in al1 t r a c conditions. Therefore, we proposed

a neuT scheme called the unified channel allocation scheme which unifies the advantages

that exist in the fked and the dynamic schemes. Vie have shown by simulation that the

UCA scheme using the virtual channel set performs better in terms of cal1 blocking than

the other schemes in both uniform and non-uniform traffic environment.

The key feature of the UCA scheme is that it recornmends the best set of channels to be

used from the VCS. It does not permanently assign some or al1 the channels to sectors as in

the fixed or the hybrid schemes. Furthemore, it does not allow the channeIs to wander al1

over the cellular network as in t.he dynamic schemes. The CCA scheme is flexible in that

the channels can be used anywhere in the network on the neod basis. It also ensures that

the channels will return to its original sector. .As a result, the UCA scheme eliminates the

drawbacks that exist in the other schemes. Moreover! the UCA scheme with the dynamic

W S is a practical scheme that can be implemented without the knowledge of the traffic

fluctuations with time and space, because the dynamic VCS periodically re-configures itself

to satisfy the timely and spatially varying traffic demand.

Chapter 6

Conclusions

In this thesis, we analyzed the resource allocation problem in wireless networks that em-

ploy CDA4.A technology in order to achieve better system performance in terms of systeni

capacity. throughput. delay. fairness and cal1 blocking probability. Since the resources are

shared by many users in wireless systems? interference management is important to im-

prove the system performance. Our resource allocation schemes control the multiple access

interference by using various techniques in CD bih wireless networks. hfult iple access inter-

ference is reduced by decreasing the number of simultaneous users and the level of activity

for del- insensitive users to improve the performance in systems with uniform user traffic

environment. hf ult iple access int erference is balanced by dparnically allocating the re-

sources over the cellular network to improve the performance in systems mit h non-uniform

user traffic environment.

6.1 Thesis Summary

In Chapter 2. we analyzed the performance of a rnulti-clas CDMA systern in a multi-

ce11 environment for the uplink. We andytically obtained the admissible regions for the

integrated services of voice and data with various transmission rates- Es/ Io requirements

and activity levels. In integrating voice and high data rate services, a larger number of

voice users has to be dropped to add one more data user in the system. because the high

&ta rate users need a receive power that is much larger than that for a c-oice user. When

the rate of the data users further increases and becomes much higher than the voice rate.

each data user that arrives and departs the system. will rapidly change the number of

available voice connections in the system, which is an undesirable system behavior.

The number of available voice connections is small when the number of high data rate

users is large. However. it is important to provide adequate voice connections because such

users still dominate the market. Therefore, when the number of data users in the system

is high, it will be wise to control the activity levels of the data users to provide enough

capacity for the voice users. However? the effect is not significant when the number of

data users is small. 'Iloreover, ive showed that the voice and data with lowcr ratio of bit

energy to interference can m k together well in the integrated services. On the other hand,

the high data rate services with real-time applications and high Ea/4 requirements do

not integate well with the voice services. In this situation. it would be useful to allocate

separate resources for voice and data services.

T h e admissible region for low and high data rate users can be increased by lowering

the activity levels. The effect of lowering the activity levels of high speed data users on

the amilable number of low speed data usen is significantly high. 5foreover. the terminals

that are used for high speed applications such as video clip transfer are usually capable

of doing additional processing compared to the terminals used for lon* speed applications

such as reading real time stock quotes. Therefore. it is benencial to adjust the activity

levels of high speed data users when they are manq: so t hat the available low speed user

connections can be significantly increased.

In Chapter 3. we analytically derived the appropriate data rates and transmit power

levels that should be allocated for high data rate Intemet users in the downlink of a CDhf.1

systern with variable-length time slots and frames. We analyzed the throughput and the

delay performance of a system with tmo transmission modes: CDM transmission mode

where the base station transmits for al1 the active users a t the same time. and TDhf

transmission mode where the base station transmits for only one active user in a time dot.

tVe showed that the average throughput decreases by 9Wo and the average deiay in-

creases ten-fold in a severe shadowing environment compared to no shadowing case. How-

ever, we also showed that the average throughput can be increased six-lold and the delay

can be reduced by about 80% when operating the system with a transmission backoff prob-

ability of 0.05. It indicates that the initial performance degradat ion in fading conditions

must have been contributed by only a small number of but very severely affected users.

The requests from the 5% of the mobile user population rvith adverse channel conditions

are delayed until their channel conditions improve. It is important to note that the trans-

mission backoff technique is better in terms of user satisfaction than the cal1 dropping

technique: however. if the channel conditions do not irnprove For a long time it will eventu-

ally lead to call dropping. As a result. these 5% of the mobiles will face either longer delays

or call dropping: however, the rest of the mobiles will enjoy a significant improvement in

the systern performance. There is a trade-off between the systern performance and the

transmission backoff probability. The operating point of transmission backoff probability

can be controlled by choosing an appropriate t hreshold on the largest transmission delay

for a data packet. The average throughput and delay performance of the system can be

improved by t ightening such a delay t hreshold.

We compared the TDM transmission mode scheduling schemes such as round-robin

and fastest-first. and the CDM mode schemes such as equal-rate and equal-power in terms

of the average throughput and delay. The average throughput of al1 four schemes turns

out to be the same. because our system is work-conserving due to the variable-Iength

time-slotted structure and the assumption that the channel conditions do not change for

the duration of each frame. However? the delay varies with the scheduling schemes and

increases with the number of mobiles. The TDM transmission mode scheduling scheme.

narnely fastest-first scheme, achieves the best delay performance among the schernes that

we studied. In general, TDkl mode schemes c m provide higher data rates and yield bet ter

delay performance. However, the CDM mode schemes outperforrn the TDhI mode schemes

in terrns of fair allocation of data rates.

The data rates that can be achieved by the users depend on the interference ancl the

receive power levels. In Chapters -1 and 5, we considered a time reuse cluster size of three

cells. Here, interference is not caused from the first tier of SLX cells which otherwise are the

major source of interference. Moreover. half of the second tier of cells do not also cause

interference. The tinie reuse cluster size of three cells reduces the interference significantly

compared to the traditional CDhIX system with reuse cluster size of one cell. As a result.

the achievable data rate mainly depends on the receive power level. Hence. it is possible

to provide a fixed rate to al1 the users by controihg the transmit power. When the time

reuse cluster size is larger than one cell, the transmission time for the neighboring the base

stations must be coordinated. To efficiently reuse the time in sectored cellular systern we

developed aIgori t hms for best-effort and real-t ime services.

In Chapter 4, a e proposed a dynamic tirne share allocation algorithm ( DTSA-Mg)

that can uniquely find the fair share of time for a given distribution of high data rate

Internet users in a cellular network. We demonstrated the effectiveness of the DTSA-.Mg

in tems of time reuse efficiency and compared it with the fked time share allocation

algorithm (FTSA-.\lg) by simulation. The efficiency of the DTSA-hlg is about 30% better

than that of the FTSA-Alg on average. Moreover, the DTS.4-A1g maintains fairness among

the users. That is, the ratio of the maximum time share to the minimum time share

allocated in the network is in the same order. If ive modify the FTSA-AIg to achieve a

better time reuse efficiency, the ratio of the maximum time share to the miriimurn time

share NiIl be very high: hence. we cannot maintain faimess. The proposed algorithm is fair

and efficient in allocating time share in non-uniform user distribution: however. it is more

complex than the FTSX-Ag. We showed by simulation that the DTSA-Alg converges

within a reasonably small number of iterations for a relatively larger cellular network wvith

few hundred cells; hence. the DTS ..\-.Mg can be implement ed in real- t irne.

In Chapter 5, we considered the high data rate real-time seMces such as video tele-

phony. We proposed a channel allocation scheme cdled the unified channel allocation

(UC.4) scheme which unifies the advantages that exist in the fixed and the dynamic

schemes. në h a ~ e shown by simulation that the uCC% scheme using the virtual chan-

ne1 set (VCS) greatly reduce the cal1 blocking probability compared to the other schernes

in both uniform and non-uniform traffic environments. The key feature of the UC.4 scheme

is that it recommends the best set of channels to be used from the L'Cs. It does not fix

some or al1 the channels with sectors a s in the fixed or the hybrid schemes. Furthermore.

i t does not allow the channels to wander al1 over the cellular network as in the dynamic

schemes. The UCA scheme is flexible in that the channels can move around the network:

however, it ensures that the channels wili return to its original sector. As a result. the

UC.4 scheme eliminates the draivbacks that exkt in the other schemes. Moreover. the

UC.1 scheme with the dynamic VCS is a practical scheme that can be implernented rvith-

out the knowledge of traffic fluctuations with time and space. because the dynamic VCS

periodically re-con figures itself to satisfy the time varying traffic demand.

6.2 Future Work

We considered the path loss due to distance and shadowing. CVe did not consider multi-

path fading in our analysis. Future work can take the multi-path effects into consideration.

kIoreovero ive assumed that the shadowing coefficients between each user and different base

stations are independent. The effect of the correlation between shadowing coefficients can

be st udied in the future work.

In Chapter 3, we did not consider the rates allocated in the previous t ime slots/frarnes

when allocating rates in the current frame. To provide a fair service to al1 the users in

the system, the previous rate allocations should be considered when allocating rates in the

cunent frame. This c m be investigated in future work.

in Chapter 4. we proposed a centralized d ~ a m i c transmission t ime coordinat ion algo-

rithm and showed that the algorithm execution time is reasonably small for a relative-

larger sectored cellular network. The algorithm execution time can be further reduced by

de-cent ralizing the algorit hm where each iteration can be implemented in a distributed

way by passing the necessary parameters.

In Chapter 3. the unified channel allocation scheme selects an arbitrary free channel

from the channel occupancy table when the corresponding virtual channel set is full a t the

cal1 arrival. Future work can investigate the possibility of choosing a more appropriate

channe1 from the channel occupancy table if the virtual channel set for the sector is full in

order to further reduce future cal1 blockings.

In Chapter 3, the transmit power from each base station is assurned to be fixed. The

users receive data rates according to the channel conditions. In Chapters 4 and 3. the

interference is minimized by using a time reuse cluster size of three cells. The base station

transmit power is changed to achieve a fixed data rate in TDbI mode transmission. Both the

transmit power and the data rate can also be changed to achieve an optimum throughpiit.

This can be studied in the future work.

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