PERFORMANCE ENHANCEMENTS OF DS-CDMA

SYSTEM FOR FIXED WIRELESS ACCESS

Benjamin Koon Kei Ng

A thesis submitted in conformity with the requirements

for the Degree of Master of Applied Science,

Department of Electrical and Cornputer Engineering,

at the University of Toronto

@ Copyright by Benjamin Koon Kei Ng 1999

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Abstract

Performance Enhancements of DS-CDMA System for Fixed Wùeless Access

by Benjamin Koon Kei Ng

Master of Applied Science, 1999.

Department of Electrical and Cornputer Engineering, University of Toronto.

In this thesis, performance enhancement of Fixed Wireless Access (FWA) system,

employing hybrid Spatial Division Multiple Access (SDMA) and orthogonal CDMA with

dynamic code docation, for both forward link and quasi-synchronous reverse link is in-

ves tigated. The static characteristic of FWA's radio Channel eases the implementation

of adaptive or highly directive antenna at the base station and directional antenna at

the subscriber site. We analytically derive the capacity equations for vaxious antenna

configurations combined with either orthogonal or non-orthogonal CDMA system. Us-

ing highly directive adaptive antenna in conjunction with orthogonal CDMA shows the

most prornising performance. In such environment, dynamic sectorization is introduced

and code allocation schemes axe proposed to assign codes for users in different areas.

Simulation results demonstrate that the best code docation scheme proposed provides

60% increase in the number of users with code assigned, over conventional fixed alloca-

tion. And with required SINR=6 dB, the outage probability is improved by 40%, when

two antennas, having same directivity while one adopts the code allocation scheme and

the other one does not, are compared.

The concept of overlapping sectors is introduced and the corresponding code allo-

cation schemes are presented. Simulation results show that this new concept increases

the number of users with code assigned, without suffering fiom the high complexity

associated with the dynamic sectors when using adaptive antennas. In particular, a

36-sectors system provides 45% improvement over conventional 6-sectors system.

Acknowledgement s

1 would like to express my gratitude to Professor E. S. Sousa for his valuable

advice and inspiration throughout the course of this research work. Many thanks go to

the people in wireless group for their suggestions and encouragement. And the financial

support fiom National Science and Engineering Research Coucil in forms of a graduate

scholarship is gratefully acknowledged.

1 a m also indebted to my parents for their love and support. Finally, 1 would like to

give highest praise to my Lord and Savior Jesus Christ, for He always gives me wisdom

and strength in my study.

Benjamin Koon Kei Ng

Januôry, 1999

Contents

Abstract

Acknowledgements ii

List of Tables

List of Figures viii

1 Introduction 1

. . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Radio Propagation in FWA 3

. . . . . . . . . . . . . . . . . . . . . . 1.2 CDMA for Fixed Wireless Access 4

. . . . . . . . . . . . . 1.2.1 Asynchronous Versus Synchronous S ystem 8

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Power Control 10

. . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Antema Architecture 11

1.2.4 Performance Cornparison of CDMA, FDMA and TDMA in FWA 11

. . . . . . . . . . . . . . . . . . . . . 1.3 Thesis Objectives and Organization 13

2 Analytical Results for Capacity of Hybrid SDMAICDMA System in

FWA 14

. . . . . . . . . . . . . . . . . 2.1 Basic Principle of Adaptive Antenna Array 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 MMSE 17

2.1.2 MSINR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Analysis Mode1 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 ReverseLi& 23

iii

2.3.1 Omni-Directional Antenna at Base Station and Directional An-

tenna ai; Subscriber . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.3.2 Omni-Directional Antenna at Base Station and Omni-Directional

Antenna at Subscriber . . . . . . . . . . . . . . . . . . . . . . . . 27

2.3.3 Fixed Directional Antenna at Base Station aad Directional An-

tenna at Subscriber . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.3.4 Adaptive Antenna at Base Station and Directional Antema at

Subscriber . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.3.5 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . 30

2.4 Forward Link . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.4.1 Omni-Directional Antema at Base Station and Directionai An-

tenna at Subscriber . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.4.2 Omni-Directional Antema at Base Station and Omni-Directional

-4ntenna at Subscriber . . . . . . . . . . . . . . . . . 39

2.4.3 Directional Antenna at Base Station and Directional Antenna at

Subscriber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.4.4 Adaptive Antema at Base station and Directional Antexma at

Subscriber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.4.5 Results and Discussions . . . . . . . . . . . . . . . . . . 41

3 Performance Enhancement of Hybrid SDMA/CDMA System in FWA 45

3.1 Dynamic Spreading Code Assignment Algorithms . Class 1 . . . . . . . . 46

3.1.1 Code Remangement . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.2 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.3 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4 Pedormance Enhancement using orthogonal CDMA system with Over-

lapping Sectors 66

4.1 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.2 Dynamic Spreading Code Assignment Algorithms - Class II . . . . . . . 68

4.2.1 Code Rearrangement . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.3 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5 Conclusions 78

5.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

References

List of Tables

2.1 Directivity for variow antenno patterns . . . . . . . . . . . . . . . . . . . 21

2.2 Va+ous base station and subscn'ber antenna configurations . . . . . . . . 30

2.3 Capacities (users/cell) for various configurations for reverse link with

SINR=6dB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.4 Capacities for various configurations for fornurd link with SINR=6 dB . 42

3.1 The code allocation table . . . . . . . . . . . . . . . . . . . . . . . 48

3.2 The code allocation table (modified) . . . . . . . . . . . . . . . . . . . . . 50

3.3 Various antenna configurations used in the simulations . . . . . . . . . . 54

3.4 The maximum nurnber of wers which can be suppol.ted at an ovtage prob-

abilitty of 5% for various configurutions . . . . . . . . . . . . . . . . . . . 57

3.5 The maximum number of users which can be svpported at blocking proba-

bilitty of 5% for various code allocation schemes . . . . . . . . . . . . . . 57

4.1 T h e code allocation table A . . . . . . . . . . . . . . . . . . . . 69

4.2 The code allocation table B . . . . . . . . . . . . . . . . . . . . . 69

List of Figures

1.1 Typical Architecture of Fixed Wireless Access . . . . . . . . . . . . . . . 2

1.2 Residential Customer Premises Equipment . . . . . . . . . . . . . . . . . 2

1.3 Spread spectrum signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Power spectral density of spread spectnun signal . . . . . . . . . . . . . . 6

1.5 Simplified system mode1 of DS-CDMA system . . . . . . . . . . . . . . . 7

2.1 Basic structure of adaptive antenna m a y . . . . . . . . . . . . . . . . . . 16

2.2 Cellular Environment in FWA . . . . . . . . . . . . . . . . . . . . . . . . 20

2.3 Omni-directional antenna . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 "pie" shaped antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.5 uparabola" shaped antenna . . . . . . . . . . . . . . . . . . . . . . . . . 22

. . . . . . . . . 2.6 Two-layers code allocation for orthogonal CDMA system 23

2.7 Area in which users are causing intercell interference to center cell: omni-

directional base station antema and directional subscriber antenna . . . 24

2.5 Reverse link calculation geometry . . . . . . . . . . . . . . . . . . . . . . 25

2.9 Six-sectors celldax system . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.10 Performance of various antenna configurations wi th non-ort hogonal spread-

ingcodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.11 Performance of various antenna configurations with orthogonal spreading

codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.12 Forward link caldation geometry . . . . . . . . . . . . . . . . . . . . . . 37 2.13 Performance of various antenna configuration with non-orthogonal spread-

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ing codes 43

2.14 Performance of various antenna configuration with orthogonal spreading

codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Users distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Example of code allocation and rearrangement . . . . . . . . . . . . . . . 53

SINR performance of various configurations for reverse link . . . . . . . . 59

Outage probability of various configurations for reverse link . . . . . . . . 60

Code histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

SINR performance of various configurations for forwaxd link . . . . . . . 62

Outage probability of various configurations for forward linb: . . . . . . . 63

Comparison between simulations and analytical results: (a) analysis: adap-

tive "pie" . (b) analysis: adaptive "parabola" . (c) simulation: adaptive

"pie" and (d) simulation: adaptive 'paxabola" . . . . . . . . . . . . . . 64

Cornparison of algorithm 14. 1-B and code-remangement . . . . . . . . 65

Example of overlapping sectors in a cell . . . . . . . . . . . . . . . . . . . 68 Example of using CAT-A and CAT-B . . . . . . . . . . . . . . . . . . . . 71

Cornparison between algorithm II-A and II-% . . . . . . . . . . . . . . . 76

Cornparisons between overlapping sectors and dynamic sectors: (a ) con-

ventional 6.sectors. (b) 18.sectors. (c) 18-sectors with rearrangement . (e)

36.sectors. (e ) 36-sectors with rearrangement. (f) dynamic sector. (g) dy-

namic sector with rearrannement . . . . . . . . . . . . . . . . . . . . . . 77

viii

Chapter 1

Introduction

With the ultimate aim of communication being 90 provide ubiquitous connections be-

tween users", wireless communication will inevitably play a major role in the develop-

ment of future communication systerns. In fact , wireless communication has received

tremendous attention over the last few decacles as rapid technological growth had taken

place and the cost of radio-based equipments decreased drastically. Added with the

liberation of new frequency spectrum, opportunities were created for wireless service

providers to compete with the existing wireline companies in offering telephone or even

rntdti-media services. As many customers are home-based or office-based, "Fixed Wire-

less Access ( FWA)" is now at tracting much interest [l]. In principle, fixed wireless access

simply refers to the use of radio to provide connections to subscribers whose positions

remain fked. Very often. the term "wireless local loop (WLL)" is used to denote fixed

wireless assess. The local loop is traditiondy defined as a copper cable connecting the

subscriber phone to the central office switch. The purpose of WLL is to replace the local

loop section with a radio path rather than a copper cable. While WLL mainly provides

telephony and relatively low-speed computing access capabilities, an enhanced form of

WLL, the Local Multipoint Distribution System (LMDS), aims at providing wider range

of services such as TV broadcasting, high speed data (e.g. internet) and telephony.

Figure 1.1 depicts a typical architecture of FWA. For FWA, base stations are

connected to a private branch exchange (PBX), which connects to a central office (CO),

which contains digital switching and network routing facilities required to connect the

radio network to ISDN and the Internet. Figure 1.2 shows the residential Customer

Premises Equipment used for LMDS. Note that NIU refers to the network interface unit

which comects the subscribers to the radio network. Also, the MPEG-2 decoder is

required for video services.

LOS path

I

Figure 1.1: Typical Architecture of Fixed Wireless Access

Downlink BW: 1.SGHz

u Uplink BW: 0.45GHz decoder

IOBaseT (c.g. cornputer) POTS (Telephone)

Figure 1.2: Residential Customer Premises Equipment

Using radio rather than copper cable has several advantages [2]. First, let us con-

sider a lazge telephone network with millions of subscribers. The infrastructure cost is

significantly lower when using radio, as the costs incurred in the process of constructing

cable network are huge compared to installing radio equipments at the ends of connec-

tion. Shorter deployment time is another advantage and the issues such as obtaining

right-of-way or permits for laying cables can be avoided. Also, FWA is less sensitive

to future growth of subscriber population. Radio terminals can be easily removed and

reinstalled elsewhere. Finally, new services can be added easily if suaicient bandwidth

is available. Thus, fiom a service provider's perspective, FWA is more attractive than

traditional wireline infrastructure in promot ing business profit and supplying telephone

services in highly populated or developing countries. To study and improve the perfor-

mance of FWA is the motivation of this thesis.

In the following section, the radio channel of FWA will be briefly discussed, since it

is the static nature of FWA radio channel which allows improvement in capacity over the

existing mobile cellular systern. Next, an overview of the performance of various multiple

access schemes in FWA is presented and Code Division Multiple Access (CDMA) will

be shown to be potentially the best candidate for future use in FWA. At the end of

this chapter, the main objectives of this thesis and the rnethodologies we use will be

presented.

1.1 Radio Propagation in FWA

Unlike mobile radio channel, in which the propagation phenomena is complicated by the

movement of the mobile, FWA propagation is substantially less complicated. In general,

three types of propagation phenomena exist in mobile radio channel,

1. Distance-related at tenuation: signal strength decreases with the distance fiom the

base station.

2. Shadowing: signal strength decreases due to refiections fiom baxriers, such as

buildings and grounds.

3. Multiple-path fading: signal aniving at the receiver via multiple propagation paths

due to reflections, causing cancelation arnong component s of the signal.

As shown in figure 1.1, FWA has fixed-t-fixed propagation path. Line-of-sight

(LOS) channe1 can be easily established because the subscriber antenna can be mounted

on building roofs while the base station hub is a high tower. Thus, the

with s m d e r loss exponent (a=2 to 3) can be used for distance-related

path loss mode1

at tenuation,

mhere Pt is the transmitted power and d is the distance from the receiver. Note that

a typical value for path loss exponent of mobile channe1 is 40dBldecade (a = 4). In

FWA, s hadowing and multiple-pat h fading are not as crit ical as in mobile diaanel due to

LOS and fixed terminals. In [7], the channel characteristics of FWA was studied and the

channel was shown to be very slowly fading, being in most situations essentidy static

over short time periods of the order of several tens of seconds. Also, it was found that

the maximum values for the urban and sub-urban high-rise environments were 410ns

and 360ns, respectively. For a system bandwidth of 1.25MHz specifiecl in IS-95', there

exists only one resolvable path.

Rainfall is another potential problem which causes significant signal attenuation

when the carrier frequency is high. At 3GHz, the attenuation for a typical FWA path

is about 0.36dB. However, at 10 GHz, the attenuation could be as high as 30 dB over

a typical link. Recently, the proposed frequency for WLL is in the range of 3.4-3.6

GHz. And for LMDS, the frequency is about 28 GHz, which will make rainfall a severe

problem.

1.2 CDMA

As in mobile cellulitr

for Fixed Wireless Access

system, the choices of communication theory techniques such as

modulation, coding or multiple access scheme, have strong impact on the performance

of FWA system. In particulax, the multiple access scheme is a dominating factor in

detennining the capacity or spectral efficiency of a cellular system.

There are three major types of multiple access schemes: Code Division Multi-

ple Access (CDMA) , Frequency Division Multiple Access (FDMA) and Time Division

lIS-95 is a standard for DS-CDMA system

4

Multiple Access (TDMA). We now quicldy review the basic principles of CDMA.

The earliest application of spread spectnun, which is the origin of CDMA, was

found in rnilitary communications half a century ago, when anti-jamming and signal pro-

tection were provided using this tedinique. With rapid technoiogical advances, CDMA

is nowadays being employed in many civilian applications, and it has been adopted as

the second North American digital cellular standard, 23-95. It is also considered seri-

ously as a viable multiple access scheme for the third generation mobile systems. The

underlying principlt: of CDMA is to make the entire bandwidth available to each user.

The resulting bit rate is many times larger than that of the original signal containing the

information (figure 1.4). This way of spreading the signal by adopting a code sequence

having higher bit rate is termed Direct Sequence CDMA (DS-CDMA) and it will be our

focus in this thesis.

First, we consider DS-CDMA system with BPSK as the basic modulation scheme.

A typical baseband data signal d( t ) , spreading signal c ( t ) and the modulated spread

spectrum signal s ( t ) at the transmitter are given by,

where d k = f 1 is the data sequence, CE = f 1 is the chip sequence, w, is the carrier

frequency, h,(t) is the chip pulse, h( t ) = 1 when t 5 T/2 and zero otherwise, where T is

the data period.

Figure 1.3 and 1.4 show the time domain and frequency domain representations

of the above signals, respectively. The bandwidth expansion is approximately equal to

T/Tc , which is equal to the processing gain G,,.

The system mode1 of DS-CDMA with Ai multiple users is depicted in figure 1.5.

(a) &ta signal

(b) sprcading code

(c) spread spectrum signal

Figure 1.3: Spread spectrum signal

, spread spccmm signai

Figure 1.4: Power spectral density of spread spectrum signal

(a) CDMA uansmitters and radio channel

dafa source of user O

cp1 cos(w,t )

v

dafa source of usGr t

.1

(b) CDMA receiver for user i

Figure 1.5: Simplified system mode1 of DS-CDMA system

O a a

b b b

Radio Channel To Rcceivcr

a a 0 A

O a O

O rn O

CM., ( C I cos( oc r ) Background Interference N t )

dafa courcr of , user N- 1 I

Assuming that n(t) is a zero mean white Gaussian background noise and multipath

interference is absent, the received signal is given by,

where Ai is the amplitude of signal, Bi and Ti are the carrier phase and delay of signal

from user i. Without loss of generality, we let the signal from user O be the desired

signal and others to be the interference. Also, let ro = O, Oi = O and assume "1" is

transmitted. The demodulation consists of despreading by the 0th user's code using a

correlator receiver. The clecision variable y at the sampling point in receiver is given by

[SI 7

The fist term is the desired signal, the second term is the multiple access interference

(MAI) due to other N-1 users, and 11 is a zero mean Gaussian random variable with

variance = y, where No is the one-sided power spectral density of n(t) .

1.2.1 Asynchronous Versus Synchronous Syst em

In a system with many users, MAI is the dominant interference over the background

noise. The key idea of suppressing MAI is to select a family of spreading codes which

has low cross-correlation and hence results in a reduced MAI term in (1.6). However,

in as asynchronous system, in which signais from different users are received without

time alignment at the chip level, it is an insurmountable task to find a set of opti-

mum codes which minimize the MAI term. Some sub-optimal sets, however, exist (e.g.

Gold sequence), which contain codes having good randomness properties and low cross-

correlat ions.

In a mobile cellular system, the reverse link is asynchronous simply because it is

inherently difficult for mobiles in motion to adjust their transmissions in a spchronous

and cooperative fashion, especidy when their signals undergo rapid fading. However,

in FWA, time misalignment c m be controlled effectively due to the static nature of

radio charnel. Hence, orthogonal spreading codes can be used in secalled "quasi-

synchronized" connection to provide low cross-correlations. In forward link, signals

for different users are transmitted syndiranously in a point-temultipoint transmission.

Synchronous transmission employing orthogonal codes can therefore be considered in

the forward liak,

Mow, we consider the effect of synchronization and the choice of spreading sequence

on the performance of CDMA system, for both forward and reverse link. Assume the

delays ri are u n i f o d y distributed between [-A,.&, A,.,T'], the MAI can be mod-

elled as Gaussian distribution with zero mean when the user population is large. The

variance is given by [13],

where ECvi is the bit energy of user i. Assuming coherent reception and BPSK with

square chip pulse, bit error rate (BER) or probability of error is given by,

where SINR denotes the signal-to-interference-plus-noise ratio, erfc(x) is the comple-

mentary error fimction and,

2 2 1 l-&raa;~x~ma* random sequence Y =

I 1 *%? orthogonal sequence

Note that h = 0, O < A < 1 and h = 1 refer to perfectly synchronous, quasi-

s ynchronous and asynchronous transmission respectively. A commonly used orthogonal

sequence is the Walsh sequence, also known as Sylvester sequence. The n = 2m order

Walsh sequences are generated recursively as follow,

Walsh sequence had been shown to be offering the best performance in a quasi-synchronous

system [13]. The corresponding expression of 7 for such sequence was also derived by the

same author in [13]. In this thesis, we assume that Walsh sequence is used as orthogonal

sequence when quasi-synchronous reverse link is considered.

1.2.2 Power Control

From equation (1.8), it cm be seen that strong signals from users near the base station

will dominate over the weak signals from remote users, resulting in general degradation of

system performance. This is known as near-far effect. Power control is used to deviate

such problem, by guaranteeing a satisfactory quality of service for each user. This can be

achieved by adjusting the transmitting power such that either the received power (power-

based) or signal-to-noise ratio (SIR-based) is kept at the prescribed target value [5] . In

the former case, the objective is to maintain the received power for each user at the same

level to compensate the iiear-far effect or signal loss due to shadowing/fading. However,

with light t r a c load, some users may enjoy higher qu&ty of service than required,

since interference is less but received power rernain the same. On the other hand, SIR-

based power control is more efficient in utilizing resources, due to the fact that users do

not transmit power more than necessary, resulting in minimizing interference to others.

However, the main drawback is the complexity incurred in such control mechanisrn, and

sometimes instability might occur. Power-based power control is relatively stable and

simple for implementation, therefore it is widely used in commercial systems, such as

1s-95.

In mobile radio channels, perfect power control is neady impossible due to fast

fading, while the loss due to power control error is signiscitflt. In [9], it was shown

that IdB of power control error leads to a capacity loss of 50 to 60%. On the other

hand, ne=-perfect power control can be achieved in FWA, as the radio channel is near

fading-free. By eliminating this main drawback inherent in CDMA system, CDMA

becomes a favorable multiple access scheme in FWA. In addition, the static nature of

FWA radio channel eases the implementation of SIR-based power control, which bnngs

about another potential benefit of employing CDMA in FWA.

1.2.3 Antenna Architecture

In CDMA, when fixed directional antennas are used at the base station, the ce11 is

subdivided into sectors and higher capacity c m be achieved by reusing the frequency

band in every sector. In 1s-95, three 120" directional antemas are employed, and almost

3 times increase in capacity is achieved [IO]. There are several reasons which facilitate

the use of more sectors and better antema architecture in FWA over mobile system.

One is the absence of hand-offs, which occurs when mobiles visit different sectors during

the duration of c d and cause lots of signalling among sectors. Another one is the

static nature of FWA channel, which eases the implementation of more sophisticated

antenna systems, such as adaptive antenna array and subscriber directional antenna.

It is therefore anticipated that innovative antenna system designs should bring about

significant irnprovement in cspacity of FWA.

1.2.4 Performance Cornparison of CDMA, FDMA and TDMA

in FWA

In TDMA and FDMA, the entire resource (in time or frequency domain) is partitioned

and allocated to different users on a time slot and fiequency slot basis respectively.

The key advantage is the disjointness between users of different channels, that is, a

perfect isolation of one user to another, which results in very little adjacent channel

interference. Due to scarcity of resources, a caxefitl allocation of resources is required

in order to reuse the resources efficiently and dynamicdy. This means that the same

frequency band or time slot is simuleaneously employed by users of different cells/sectors

according to a certain reuse pattern. In FDMA, the distance between cells reusing the

same resource is called frequency reuse factor. A shorter distance, or smaller reuse factor,

indicates that the same resource can be reused more often and hence provide services to

more users over the same geographical area. It is worth mentioning that both FDMA

and TDMA sufter some drawbacks. First, neighboring cells reusing the same sources

should be separated apart by some minimum distances, such that the mutual intercell

interference do not exceed a certain threshold level. Hence, it poses a limitation of the

srnadest time or frequency reuse factor and results in lower capacity. Also, bandwidth

expansion is another problem, since guard intervals in both time and frequency domain

are required to minimize adjacent channel interference. And to adapt to various trafic

conditions, dynamic resource management should be employed, but coordination among

neighbouring cells is a complicated issue to be addressed.

On the other hand, CDMA has inherent interference-rejection capability. Through

the despreading process, the strong MAI can be effectively suppressed. This allows the

frequency reuse factor to be equal to one. In other words, CDMA can tolerate much

higher intercell interference, compared to other two schemes. Also, in an environment

with multipath propagation, the large bandwidt h provides diversity gain through corn-

bining constructively multiple signal paths using a R.4KE receiver [6]. However, CDMA

also suffers some major drawbacks as discussed above, such as enor occurred in power

control, using non-orthogonal as supposed to orthogonal codes due to asynchronous

reverse channel.

In mobile cellular system, it is well known that there exists difficulties in making

a fair comparison between CDMA, FDMA and TDMA in terms of capacity (number of

users/cell) in a multicell environment. Their performance will depend on the availability

of technologies, the environment, and various assump tions made. However, when t hey

are considered in the context of FWA, CDMA outperforms others by having most of its

disadvantages effectively alleviated, as discussed previously. Some outstanding features

axe now highlighted as follows,

1. FWA dows more accurate power control and hence the capacity can be increased.

2. Frequency factor is equal to one and no frequency planning among different cells

is required.

3. CDMA is interference-limited scheme. B y using highly directional or adaptive

antennas at both ends of a radio link, the interference is mitigated effectively.

Because of single frequency reuse, the link capacity c m be linearly proportionate

to the decrease in interference power.

4. The nature of FWA radio channel allows the reverse link to be quasi-synchronized.

Thus, orthogonal codes can be used to further suppress the intracell interference

caused by other users.

Therefore the above reasons motivate the research for a CDMA system better than

the existing 1s-95 standard when FW.4 is considered. In this thesia, the focus will be on

the latter two features.

1.3 Thesis Objectives and Organizat ion

In this thesis, we propose and analyze new methodologies to achieve performance en-

hancement of DS-CDMA system in FWA. The new methodologies are primarily based

upon a combination of Space Division Multiple Access(SDMA), orthogonal DS-CDMA

system and dynamic code allocation. SDMA simply refen to using adaptive antennas

at the base station to spatially resolve signals arriving at different angles. With the exis-

tence of fked propagation paths and limited spatial variation of arriving signals, SDMA

is more feasible in FWA than mobile system. The improvement in capacity due to the

utilization of both SDMA and CDMA will be found analytically in chapter 2. In chapter

3, several code allocation algorithms are proposed for orthogonal CDMA system. As we

will show, the issue of code allocation must be addressed when orthogonal CDMA is

used in conjunction with SDMA. In chapter 4, the concept of overlapping sector is in-

troduced, followed by the discussion of corresponding code ailocation schemes. Finally,

conclusions and future research directions are presented in chapter 5. 8 8

Chapter 2

Analytical Results for Capacity of

Hybrid SDMAICDMA System in

FWA

As cliscussed in Chapter 1, multiple access interference (MAI) due to other users causes

severe signal distortion and limits the capacity of DS-CDMA system. One effective

way to mitigate MAI is to exploit the spatial filtering properties of adaptive antenna

array. Adaptive antenna array comprises a set of spat idy distributed antenna elements,

the output of which are combined adaptively such that their directional patterns will

maximize the signal-tenoise ratio of a desired signal. It is therefore possible to extract

the signal of a desired user, while spatidy filtering out MAI from other users, even

though they dl occupy the same signal space (time dot, frequency band or spreading

code). This leads to the concept of Space Division Multiple Access (SDMA)', which

can be combined with FDMA, TDMA or CDMA to yield significant irnprovement in

performance. This chapter is devoted to the discussion of the principle of antenna

mays and the analysis of capacity when hybrid SDMAICDMA is employed in FWA. To

compare and contrast the performance between SDMAICDMA and conventional CDMA

system, vie investigate various configurations of base station antenna and subscriber

' From now on, SDMA refers to the use of adaptive antenna array and vice versa

antema combined with either non-orthogonal or orthogonal spreading codes. It is also

worth noting that past researchers had only focused on either orthogonal CDMA or

SDMA with non-orthogonal CDMA [19] [20].

2.1 Basic Principle of Adaptive Antenna Array

Figure 2.1 depicts a typical configuration of adaptive antenna array, which combines

and dynamicdy acljusts the weight for each element utilizing a feedback mechanism.

Generally speaking, performance improvement increases with the number of antenna

elements in the array, which represents a higher degree of flexibility in configuring the

radiation pattern (such as smaller main beamwidth). Therefore, there exists a trade-off

between the cost of radio equipment and the gain in performance. In this thesis we

implement an adaptive antenna array assuming that both complexity and cost can be

justified. As the user terminais should remain relatively simple due to cost, size and

power? it appears that base station is the only plausible place to trade off complexity for

performance gain. Therefore only base station antenna array is considered. Specifically,

we confine our attention to the reverse link, although the theoy applies to forward link

as well.

Using complex baseband representation, the received signal at each antenna ele-

ment is given by, Ns- 1

- h &d(m-&)sin 19 x m ( t ) - m C si(t) + n m ( t )

where m deuotes the element number, hm is the elernent response, 0 is the angle or

direction of arrival (DOA), d is the distance between two adjacent antenna elements, Ns

is the total nurnber of users, s i ( t ) is the analytical representation of DS-CDMA signal

from user i, and n,(t) is the analytical signal associated with the background noise at

element m. We assume that the anay elements are linearly arranged with inter-element

spacing equal to X/2. The general e-pression of x, (t) in terms of the positions of antenna

elements can be f m d in [19]. And the data vector containing all the received signals is,

the output of the adaptive antenna axray is given by,

where W is the weight vector applied to the received signal.

The optimal weight vector can be derived subject to two different criteria; (1)

rninimizing the mean square erra ( MMSE) , (2) maximizing the signal- to-interference-

plus-noise ratio (MSINR) .

element 1

front end receiver

7 I

front end receiver

front end receiver

- .

1 Adaptive I

Figure 2.1: Basic structure of adaptive antenna array

2.1.1 MMSE

Using this approach, an error signal e is obtained by subtracting the m a y output from

a reference signal c d e d r(t). Therefore, the weight vector is driven to optimal subject

toy

min E [ E ( ~ ) ~ ] = min E[( r ( t ) - ~ ( t ) ) ~ ] (2.4)

where E[-] refers to expected value. Hence, the optimal weight vector WOpt is (see

reference [18] for detailed derivation)

W,, = W'S' (2 .5)

where

where ( -)* and denote the complex conjugation and transposition respectively. To

adjust the weights adaptively to reach WOpt, several algorithms have been proposed,

such as Least Mean Squares (LMS), Recursive Least Squares (RLS), and the Constant

Modulus Algorithm (CMA). They all require a reference signal which is correlated with

the desired signal. The method to extract the reference signal affects the system's

performance and is therefore an important research issue.

2.1.2 MSINR

Next we consider the optimal weight for miwcimizing the SINR, that is,

where Xd(t ), Xi(t) and N(t) are the desired signal component, MAI component and

background noise component respectively, which are contained in X( t ) . The m a y output

due to the desired signal component can be expressed as,

where a is a constant, and the steering vector Ud is given by,

Ud contains the inter-element phase shift &, which is a function of the direction of

arrival (DOA) and the inter-element distance ((bdm = d(m - 1) sin 8, for linear array) . Finally, the optimal weight vector is given by (see Appendix A for detailed derivation),

where p is a scalar constant and <Pu = E [ x ~ ' ( ~ ) x ~ ~ (t)] + E[N'(~)N~(~)] is the covariance

matrix of undesired signals only.

Hence, on the contrary to the aforementioned MMSE algorithms which require a

reference signal for updating the antenna weights, algorithms based on this approach

requires the knowledge of direction of arrival (DOA) . During adaptation, the steering

vector is required and is derived by the estimation of DOA. Several methods for esti-

rnating DOA were reported in the literature, such as MUSIC [Il] and ESPRIT [12]. It

was discussed in [15] that the error incurred in the estimation of DOA would introduce

significant degradation in pedormance. In a system with high velocity users and fast

fading, the impairment due to error in estirnating DOA is severe. However, with the

existence of LOS in FWA, DOA can be precisely obtained (e.g. through a separate chan-

nel), and thereby making adaptive antenna array using this approach very attractive in

FWA applications. In fact, this thesis will focus on utilizing this methodology, assuming

that the DOA is hown o priori.

To simplify our performance analysis of adaptive antenna Wray in FWWA, it is as-

sumed that the adaptive algorithm and the associated hardware to implement these

system can be realized. The determination of optimal weights from a signal processing

point of view would not be part of the analysis. Instead, it is assumed that an optimum

radiation pattern is formed at the base station a n t e ~ a for each subscriber in the system.

In FWA, the beam pattern is tradring the strongest signal path (LOS) containing most

of the transmit ted signal energy to achieve better SINR. Therefore, we claim that steer-

ing the main beam towards the direction of desired terminal is an optimum radiation

pattern. In other words, the maximum gain of radiation pattern is pointing towards the

desired subscriber. iUso, the performance of SDMA can be further enhanced by using

fked directional antenna at the subscriber terminal. Since the motion of subscriber is

restricted, the subscriber antenna can be steered towards the direction of target base sta-

tion. The following andysis will calculate analytically the capacity in WLL for wious

combinations of antenna configurations in both reverse and forward link.

2.2 Analysis Mode1

We proceed by defining a typical hexagonal cell arrangement, as shown in figure 2.2. It

consists of a center ce11 (0th cell), for which the capacity is derived? and the surrounding

two tiers of interfering cells. We also classi@ the interfering cells into 3 groups, A, B

and C? as shown in figure 2.2. The base station is located at the center of each cell. To

simplie the following calculations, we will use circular cell to approximate hexagonal

cell by replacing a circle having the same area as the corresponding hexagon, hence the

radius of the cell R is given as,

where T is shortest distance fiom the center to the edge of hexagon.

Subsnibers are unifonnly distributed over each cell. We assume that the received

power is only a function of distance, governed by the following path loss law,

where d is the distance between transrnitter and receiver, Pt and Pr are the power

transmit ted and received respectively.

Figure 2.2: Cellular Environment in FWA

Shadowing and fading are neglected for this first-order analysis. Thus, users would

communkate with the neares t base station, which also provides the strongest received

signal.

Figures 2.3-2.5 illustrate various ideal antenna radiation patterns that will be used

throughout this analysis for both forward and reverse link. Figure 2.3 is the conventional

omni-directional antenna with uniform antenna gain spanning over the azimuthal angles.

Figure 2.4 is the "pien or "wedged" shaped antenna with ideal 60' main lobe of uniform

gain and no sidelobes. The use of antenna with relatively broad 60' main beamwidth is

reasonable due to the limitation on complexity and cost of subsciiber terminal. Finally,

1 Antenna I Directivity I

1 Adaptive "pie" 17.78dB I

L

Omni-directional Static "pie"

- - 1 1 Adaptive " parabolan 1 9.70 dB

"

O dB 7.78 dB

Table 2.1: Directivity for variow antenna patterns

figure 2.5 is a "parabola" shaped antema having the maximum gain at the center and

diminishing gain dong the main lobe. Note that these antenna patterns can be either

fixed or adaptive, depending on whether it can be steered towaxds the desired direction

adap t ively.

Furthemore, to assess the performance of individual antema pattern, we define

the directivity (D) as follows, 2n

D E dB f,'" G(@)

where G(B) is the antenna gain as a function of signal impinging angle 8. Table 2.1 lists

the directivity for different antenna patterns.

Figure 2.3: Omni-directional antema

Figure 2.4: "pie" shaped antexma

Figure 2.5: "parabola" shaped antenna

2.3 Reverse Link

In this section, the capacity of the reverse link will be derived analyticdy. IR FWA, we

assume perfect power control c m be reaiized, that is , all users adjust their transmitting

power such that their received powers at the base station axe the same. By normalizing

the received power to be 1 for each user, the transmitting power according to (2.13) is

The signal-to-interference-plus-noise ratio (SINR) is

SINR = ~ I i n t r a f [inter +

given by,

(2.15)

where Gp is the processing gain, Iintra is the intracell (in-cd) interference, Iinter is the

intercell (out-of-cell) interference, i ) is the background noise interference and y is the

interference reduc tion factor given by (QPSIi modulation wit h sinc chip pulse),

Non-ort hogonal codes. Y =

( 0.1185, orthogonal codes wit h 1 /2 chip quasi-synchronization.

The exact value of y for i chip quasi-synchronization is obtained in [ZO].

Wlen orthogonal CDMA is considered, two-layers code allocation is employed as

illustrated in figure 2.6. Within the sarne cell, each subscriber uses a unique orthogonal

code. Further, an unique non-orthogonal code is assigned to ail users in sarne cell such

that users fiom cell to cell are non-orthogonal.

Figure 2.6: Two-layers code allocation for orthogonal CDMA system

2.3.1 Omni-Directional Antenna at Base Station and Direc-

tional Antenna at Subscriber

The capacity is now derived when omni-directional antema is used at base station and

60° "pie" shaped directional antenna (figure 2.4) is used at the subscriber. The intercell

interference fiom users of other cells to a user in the 0th ceU is fmt calculated.

Since the directional antenna is used at the subscriber terminal with main beam

pointing at the desired base station, some users in the neighbouring ceils do not cause

intercell interference to those in the center cell. Figure 2.7 shows that the intercell

interference is only contributed by users in certain areas, according to their the positions

and distances fiom their own base stations. Only those with radiation pattern covering

the center ce11 base station are included as intercell interference.

Example: user 2. Lies in shnded region, hos its berim affecting the base station of centre cell, while user 1 beam pattern is pointing riway fmm the base station of centre cell,

Figure 2.7: Area in which users are causing intercell interference to center cell: omni- directional base station antenna and directional subscriber antenna

Figure 2.8 shows the geometry for caldating intercell interference. D denotes the

distance between the 0th base station and jth base station with which interferer i is

cornmunicating. rij denotes the distance between subscriber i to its base station j. dio

represents the distance between the subscriber i and 0th base station. Assuming that

the received power at jth base station form subscriber i is 1, the transmitting power

of subscriber i is therefore r:. Hence, the received power at the 0th base station from

interferer i is (z)3, according to (2.13).

Figure 2.5: Reverse link calculation geometry

R e d that users are u n i f o d y distributed over each cell and let Ns denote the

number of subscribers in each cell, the total interference Icdl caused by users in one

neighbouring cell is,

where p = $$ is the subscriber density and Gs(d) is the subscriber antema gain given

~ Y T

-1 D 1, if 101 <= n/6, where 0 = sin sino). Gs(B) =

( 0, otherwise.

Note that 6 and ,O are both defined in figure 2.8. And dio is given by,

where D = 2T, Group A cell.

D = 2 & ~ , Group B cell.

D = 4T, Group C cell.

After some mat hematical manipulations, (2.17) can be rewri tten as,

where

Due to the complexity of integral, we resort to numerical integation. And the results

for users in A, B and C type of cells are given by,

Therefore the total intercell interference is,

Next, we consider the intracell interference upon subscriber m received by base station

O. Since the received power from different subscribers in 0th cell are a l l equal to 1,

the intracell interference is simply given by fi - 1. Note that the intracell interference

is independent of the type of antenna used at the subscriber terminal. Therefore the

received SINR is,

SINR = GP y (Ns - 1) + 0.0414Ns

Note that we assiune the number of users is large enough such that the background noise

has negligible effect on the overd performance or 7 = 0.

2.3.2 Omni-Directional Antenna at Base Station and Omni-

Directional Antenna at Subscriber

The intercell interference can be found in the same way as in the case with directional

antenna at subscriber terminal. By set ting Gs(Q = 1 in (2.17), linter is found as,

Since the intracell interference is the same as if directional antenna is used at the

subscriber, the resulting SINR is simply given by,

SINR = ?(IVs - 1) + 0.7008Ns

2.3.3 Fixed Directional Antenna at Base Station and Direc-

t ional Antenna at Subscriber

Instead of omni-directional base station antenna, we consider the performance of k e d

directional antenna. Assuming six 60' "pie" (figure 2.4) directional antennas are de-

ployed, each cell is therefore divided into six sectors, as illustrated in figure 2.9. By

inspection, the interference axea is now reduced by a factor of 6. Hence, the intracell

and intercell interference are decreased accordingly. The received SINR becomes,

SINR = i 7 ( N s - 1) + i0.0414Ns

Figure 2.9: Six-sectors cellular sys tem

2.3.4 Adaptive Antenna at Base Station and Directional An-

tenna at Subscriber

Up until now, the adaptive antema m a y at the base station has not been considered.

R e c d that the radiation pattern of base station adaptive antenna array GB(+) wiU be

adjusted such that its maximum is directed towards the desired user. Both "pie" and

"parabola" antenna (figure 2.4 and 2.5) wilI be used as adaptive radiation patterns.

Although with perfect power control, the received SINR will v a q from user to

user due to the adaptive beam pattern. Let's hs t consider the effect on the intercell

interference due to one interfering cell. Referring figure 2.8 for calculation geometry,

it becomes clear that the amount of intercell interference depends on the DOA (a) of

desired signal from subscriber m in ce11 O. Hence,

and

Then, the expected Icerl over all possible a is given by,

Using the definition of directivity (2.14), (2 .SI) becomes,

where Zcer1 is given by (2.17) (Le. when omni-directional base station antenna is used).

Thus we see that directivity indicates the amount of interference reduced by using the

adap t ive antenna over omni-directional antenna.

Next, the intracell intederence received when using base station adaptive antema

is given by,

Therefore the expected SINR can be expressed as,

2.3.5 Results and Discussions

We now consider five antenna configurations using the above capacity equations:

I antenna pattern I Configuration

1

Table 2.2: Vari0.w b u e station and subscriber antenna configurations

3 4 5

Gp is assumed to be 125. Figure 2.10 shows the received SINR versus number of

users N per cell for different combinations of antema using non-orthogonal spreading

codes (i.e. asynchronous transmission). Table 2.3 contains the capacities for various

configurations assuming the required SINR=GdB. Note that the required SINR=GdB is

a very conservative assumption, since no coding gain is considered yet. For example,

in (11, the required SINR was shown to be 4dB when 112 convolutional code with soft

decision coding is employed.

It is clear that the case with omni-directional base and subscriber antema exhibits

the worse performance. With the addition of directional subscriber antenna, the capac-

ity improves by 93%. Note that this improvement is due to the significant alleviation

base station ornni-direc t ional

subscriber terminal fixed "pie" shape

fixed "pien shape adap t ive 'Lpie" shape

adaptive "parabola" shape

fixed "pie" shape fixed "pie" shape fixed "pie" shape

Antema 1 Reverse link 1 Reverse link 1 Orthogonal 1

Table 2.3: Capacities (wers/cell) for various configurations for reverse link with SINR=6 dB

configuration 1

of intercell interference, not intracell interference. Also, as expected, using a 60' di-

rectional base antenna results in six-fold increase in capacity. It is worth noting that

(non-orthogonal) 19

the performance of "pien adaptive base station antema is the sâme as that of "pie"

fked directional antenna, since they have equal directivity. Although adaptive antenna

(orthogonal) 38

possesses the unique advantage of steering towards the subscriber, it does not bring

code lirnit 128

about significont improvement over the static antenna if the subscribers are unifonnly

distributed. Findy, the "parabola" antema offen the highest capacity due to its high

direct ivity and spatial resolveability.

Figure 2.11 shows the performance of vztrious combinations of antema with orthog-

onal spreading codes (i .eV quasi-s ynchronous transmission) and Table 2.3 cont ains the

corresponding capaci ties. Using orthogonal spreading codes results in h o s t six-fold

increase in capacity over non-orthogonal spreading codes when subscriber directional

antennas are used. Without subscriber directional antemas, only two-fold increase is

attained. This c m be explained by the fact that orthogonal codes only reduce the intra-

cell interference. Without subscriber directional antenna, the intercell interference is s till

significant, which results in slightly less improvement than using subscriber directional

antenna. Nevertheless, orthogonal CDMA is ideal for FWA. It is, however, important to

realize that results in figure 2.11 are obtained without considering the lirnited nurnber

of orthogonal code, which is 128. For the six-sectors configuration, each code can be

reused in each sector, resulting in total 768 (=6 x 128) available codes. From Table 2.3,

the capacity is found to be 1175 users/cell, which already exceeds this hard limit. Code

limitation poses a significant problem for orthogonal CDMA system. This is also true

for the case of adaptive antennas. The capacities shown in Table 2.3 for configuration 4

and 5 are 1175 and 1833 users/cell respectively. Again, whether these capacities can be

achieved remains questionable, once the code limit is taken into consideration. When

using adaptive antennas, the number of a d a b l e codes are not known due to the absent

of fixed sectors. The answers to where and how fiequent the orthogonal code is reused

are yet to be found. Therefore, motivated by the potential capacities achieved using

these antemas, the issue of code reuse has to be addressed. We postpone this discussion

to chapter 3, and now continue on to the analysis of forward link.

config. 1 . - . - . - confis. 2 .

0- config. 3 U config. 4

config. 5 -

Figure 2.10: Performance of wious antenna configurations with non-orthogonal spread- ing codes

config 1 . - . - - -

. . config. 2 - 0 config. 3

config. 4

. . H config. 5

Figure 2.1 1: Performance of various antenna configurations with orthogonal spreading codes

2.4 Forward Link

The capacity of forward link is also very important in FWA, since the volume of down-

s tream traffic will likely be greater than the upstream t r a c for services such as inteinet

or TV broadcasting. In this section, the capacity is malytically derived applying the

same assumptions used in reverse M.

The SINR for subscriber i is given by,

where Pi is the received signal power of desired user, Iintra is the received signal power of

other users transmitted from the same base station and Iinter is the interference power

from other base stations. One notable difference between the reverse and the forward

link is the value of y. In the forward point-temultipoint transmission, synchronization

at the chip level is easier achieved than in the reverse link. The interference reduction

factor is expressed as follows.

1, non-ort hogonal codes.

0.05, orthogonal codes.

The residue value of y for orthogonal code is estimated [4] to account for imperfections

due to hardware and radio Li&.

When orthogonal CDMA is considered, s e again refer to twdayers code allocation

as ihstrated in figure 2.6. Intracell interference received by a subscriber is due to

orthogonal signals fiom other subscribers within the same cell, while intercell interference

fiom other base stations is non-orthogonal.

2.4.1 Omni-Directional Antenna at Base Station and Direc-

tional Antenna at Subscriber

We assume that the same "pie" directional antenna is used for both receiver and tram-

mitter at the subscrïber temiinal, and omni-directional antema is used at the base

station. Let STj be the total power that j t h base station transrnits. A fraction of STj

is allocated to the subscriber i; whose nearest base station is j, with the remainder of

Sc and total received power fiom other base stations being received as interference.

Using the simple path loss mode1 (2.13), the received signal power due to individual ST- base station is SR, = $, where dij is the distance between subscriber i and the base

station j. Let us consider subscriber i situated in the center cell O. The received SIN&

is given by,

where 5 is the fraction of total base station power devoted to subscribers (1 - 6 is

used for pilot), 4i is the fraction of this docated to subscriber i, y is the interference

reduction factor, and k is the number of interfering base stations. Assuming rl is very

small compared to base station power and by remanging (2.37), 4; is given by,

And since 4i is the fraction of total available power subscriber i consumes, the sum of

these fractions for all subscribers must not exceed 1. We therefore have the following

constraint ,

where Ns is the total number of subscribers in the same cell. By combining (2.38) and

(2.39), we obtain the following,

Ns-1 SINR (d?+z-)<l SR,

i=O j=i S~ii

Note that we have assumed al l users require the same quality of service (Le. SINR, =

SINRj for a l l i and j ) . Now, by expanding (2.40) and interchanging the summation

order, the following result s,

< J(Gp + 'YSmR) - SINR - Jy Ns

Next, we define the relative received power measurements with respect to base station j

Thus, using fi in (2.41), (2.37) becomes,

k s(Gp + SINRr) Cfj 5 SINR - WS j= 1

To compute the maximum iVs subject to the above constraint is equivalent to calculating

the capacity. We proceed by calculating fi for different interfering cells. Figure 2.12

shows the capacity calculation geometry.

Figure 2.12: Forward link calculation geometry

For the sake of simplicity, the base station transmitted power STj is nonnalized to

1 for aU j , since each hase station is serving the same number of subscribers in each cell.

Summation in (2.42) is done by integrating over the cell area with user density p = S. Thus, f, can be expressed as,

-1 & 1 if 14 1 < = n/6 where 0 = sin ( d . . sin p) Gs(0) = 'J

( O otherwise

Libre the reverse link, (2.44) can be simplified as,

where u ( r ) is also given by (2.22). fj is then computed for three types of ceus. Using

(2.20) and (2.46), we found,

fj z 0.0049Ns j E Group A cell

fj 2: 0.0012Ns j E Group B ceIl

fj 0.0008Ns j E Group C cell

and

Using (2.41), the capacity is given by,

2.4.2 Omni-Directional Antenna at Base Station and Omni-

Directional Antenna at Subscriber

We now obtain fj for the case when omni-directional antema is used at the subscriber

terminal. Following the procedure similx to that of the reverse link,

Using the above value and (2.43)? the capacity is given by,

2.4.3 Directional Antenna at Base Station and Directional An-

tenna at Subscriber

Next, we assume that ideal directional base station antenna is deployed and hence the

cell is divided into 6 sectors (figure 2.9). The total power ST transmitted by each base

station is equally dlocated to different sectors. Since the sectors are disjoint and ideal,

the intracell interference and intercell interference are reduced by a factor of 6. Therefore

(2.37) can be modified as follows,

Using (2.52) and perf'orm the similar analysis as in the case of omni-directional base

station antenna, the capacity of six-sectors system is given by,

2.4.4 Adaptive Antenna at Base Station and Directional An-

tenna at Subscriber

Findy, we consider the use of base station adaptive antenna and study its performance.

Let us fist investigate how the base station adaptive astexma affects the intercell interfer-

ence experienced by the subscriber i in cell O, as shown in figure 2.12, when the radiation

pattern of the adaptive antennas Ge (v) has its maximum gain directed towards the de-

sired terminal (il in this case) in its own cell. The total interceil interference generated

by the base station in a neighbouring cell j and received by subscriber i is given by,

where iVs is the number of subscribers in the interfering cell, and v, is the angle between

subscriber na of interfering ce11 and subscriber i of the centre ceil, as shown in figure

2.12. The location of subscriber rn c m be represented by (r,v,), where r is the radial

distance from base station j. Let p denotes the uniform subscriber density, (2.54) can

be expressed as,

and the constraint (2.39) becomes,

For the sake of sirnplicity, we made the approximation that +(r, v) ci +(r) . That

is, the allocation of power mainly depends on the distance from the base station, not

the DOA. This is reasonable as the power attenuation is a function of distance and

the geometry of the cellular systern is symmetricd, making the DOA less relevant in

allocating power. Thus, (2.56) becomes,

Using (2.57), (2.55) becomes,

where SRij = STjdW3. This result implies that the amount of intercell interference pro-

duced by using base station adaptive antema is less than that of using omni-directional

by a factor of D. Note that we can

Therefore, we simply replace SRij by

subscriber i as follows,

extend the above results to intraceu interference.

Sai, in (2.37) and obtain the the received SINR by

and the capacity is,

!V, 5 J(G, + SINRr D-L) SINR PL (0.0069 + 67)

2.4.5 Results and Discussions

Figure 2.13 and 2.14 shows the SINR versus number of users for various combinations

of antema, with orthogonal and non-orthogonal spreading codes respectively. Table

2.4 shows the capacities according to the required SINR=GdB. Like the reverse lid,

directional subscriber a n t e ~ a provides significant improvement over conventional omni-

directional antenna. At the base station, the "paabola" antenna offers the highest

capacity over the others. On the other hand, the adaptive "pie" and fixed 'pie" antenna

provide almost the same performance. It again shows that given the same directivity

and homogeneous trdfic, adaptive antenna does not outperform its static counterparts.

The use of orthogonal codes is also shown to be more favorable. In cornparison with

1 Antenna 1 Forward link 1 Forward link 1 Orthogonal 1

4 180 1940 variable

configuration 1 2

1 5 1 280 1 3000 1 variable 1 Table 2.4: Capacities for variow configurations for forwurd link with SINR=6 dB

(non-ort hogonal) 19 31

the reverse link, the potential capacity (without considering the code lirait) of forward

link is substantidy higher when orthogonal codes are employed. This is due to the

(orthogonal) 38 322

existence of highly synchronous orthogonal forward link with very s m d MAI. However,

code limi t 128 128

the detrimental drawback is the shortage of spreading code, which poses the capacity

limit for high performance antennas.

Figure 2.13: Performance of various antema configuration with non-orthogonal spread- ing codes

Figure 2.14: Performance of various antenna configuration with orthogonal spreading codes

Chapter 3

Performance Enhancement of

Hybrid SDMAICDMA System in

FWA

In this chapter, the overd capacity of FWA system employing both SDMA and CDMA

will be evaluated by means of Monte-Carlo simulations. The simulations will be used to

veri@ the analytical results obtained in last chapter. From last chapter, it was shown

that orthogonal CDMA shows drastic improvement over its non-orthogonal counterpart.

However, when non-orthogonal CDMA utilizing random sequences is employed, SDMA

can be naturdy integrated into the system, since the number of anilable codes is not

limited. In that case, it is not necessary to reuse the same code within the same ce11 by

spatial division. But the situation is different for orthogonal CDMA system in which the

codes must be reused within the same cell to achieve high spectral efficiency. Moreover,

there exists the issue of dynamic sectorization if SDMA is to be implemented. These

reasons suggest that new code assignment schemes should be devised. We therefore

propose several code allocation aigorithrns for use with SDMA , and compare their

perfomances in t ems of improvement in capaci*

3.1 D ynamic Spreading Code Assignment Algorit hms

- Class 1

We now limit our discussion to orthogonal CDMA FWA system. When eadi user estab-

lishes its own connection with the base station via the base station adaptive antenna,

we c m define the so-called dynamic sector, which is generated for each user. Each sub-

scriber terminal is located at the center of the sector as if it defines its own sector. The

purpose of dynamic code assignment schemes is to achieve high spectral efficiency with

dynamic sectorization, while maintainhg the required orthogonality among ccxhannel

users.

To achieve the above purpose, it is necessary to reuse the same orthogonal spread-

ing code among different users in an efficient rnanner (as many times as possible), while

keeping these "CO-code" users sdiciently apart so as to avoid mutual interference. Hence,

two questions need to be answered: 1) which codes are available for use when a new user

enters the system? 2) which code should be chosen among the available ones in order to

achieve good code-reusability or increase the possibility of the code being reused again

by future users'?

To address the first question, we proceed by introducing a parameter c d e d min-

imum reuse angular separation (MRAS), which is defined as the minimum required

angular separation between two user terminals which use the same code. MRAS thus

determines the size of a user's sector. Obviously, antema pattern having narrow beam

width with small side lobes' gain corresponds to a small MRAS and high reuse efficiency.

In addition, a lookup table or code allocation table (CAT) is used which contains all the

active users1 locations (in t ems of azimuthal angle with respect to base station) with

the corresponding codes which they are currently using and those which are prohibited

from use (see table 3.1). The purpose of CAT is to store information about the current

adab i l i ty of spreading codes for incorning users. In CAT, each row corresponds to one

active user, while each column represents the status of individual spreading code. Let i

be the new user. From CAT, d users who are within the MRAS of new user i and the

'"active usersn refer to those who are dready in the system before the new one arrives

codes they are using can be identified. User i will find these codes unavailable. To show

this, an X is marked in an entry of CAT, Say (i, j ) , to indicate that the corresponding

spreading code j (column) is unavailable for user i (row). The new user is only allowed

to select fiom the remaining clear entries. After this user is assigned a code, Say k, an

O is then marked in the entry (i, k). This is to acknowledge the future users that code j

is used by i. The above procedures then repeat for each new incoming user.

To address the remaining question regarding the appropriate choice of code, we

have proposed two different algorithms, namely I-A and I-B (see beiow), and study the

effect they have upon the spectral efficiency. In algorithm I-A, the basic objective is

to maintain average separation between users using the same code as small as possible.

The new user chooses to reuse the code currently employed by the nearest2 active user.

Assuming that no code reassignment is performed for active users who are already ad-

mitted into the system and the positions of future users are unknown, this strategy is an

effective way to achieve s m d separation. It is also a channel packing approach, which

aims at increasing the number of codes offered per unit area by minimizing the reuse

distasce. One drawback of such approach occurs when the antema pattern has strong

sidelobes. Intuitively, the separation between co-code users should not be too close, for

co-channel interference received via the sidelobes is severe. But in our analysis, the pa-

rameter MRAS already prevents such situation from occurring, since the assumption is

that co-code users having angular separation greater than MRAS cause negligible inter-

ference upon one another. On the other hand, the second algorithm proposed, I-B, will

simply choose the code randornly fiom the available ones and does not take the spectral

eficiency into account . Now, we define that is the set of all active users who lie within MRAS of user

i. The first docation algorithm (we c d it I-A) is summarized stepwise below,

1. When a new call request arrives, Say i, information about its location is sent to

the base station and recorded by adding an extra row i to the CAT.

2. Use CAT to check whether the angular separation between the new user and other

2Nearest here means the smdest angular distance

active users is below the MRAS. If yes, proceed to 3. Otherwise, all codes axe

available for use, go to 4.

3. The codes currently employed by users in Mi are restricted from use by the new

call. Mark a n X in the corresponding entry (i, k), where k is the prohibited code.

The remaining clear entries form a Est of the possible codes for the new c d . If no

code is possible, the call is blocked. Otherwise, proceed to 4.

4. For each possible code, find out from CAT which usen are currently using it. The

new c d would select the code which is currently used by the nearest terminal.

Mark an O in (i, j ) , where j is the code chosen.

Code I

Table 3.1: The code allocation table

user 1 2 3

The second algorithm (1-B) is a slight variation of algorithm LA. Steps 1 to 3 in

1-B are identical to those of 1-A, and step 4 is given below,

4. The new c d would select the code randomly from the available code pool. Mark

an O in the chosen code's entry.

3.1.1 Code Rearrangement

location(degrees) 10 136 143 --

1

x

To further increase the capacity or code reusability within a cell, we propose a rear-

rangement scheme in addition to the above algorithms. When a new user =ives, it

follows previous algonthms to find an available spreading code. If a code is found, code

remangement is not necessary. Otherwise, code remangement among the active users

is required such that the blocked user is dowed to seize the spreading code from a

2

-- 6

2 O

4

new user i

X

x x

3 x

----- x

. . . . . . . . . .

. S . . .

O

4

x o x x

X

5 X

O

* . -

x

* * - 128

x

m e n t l y active user, whom we c d it donor. Since the donor restricts the new user

for using its code at the beginning, the donor must therefore be within the MRAS of

the new user. And it must have at least one other available code for itself to switch to,

otherwise it will be forced to terminate while the c d in progress.

The objective of any code rearrangement scheme is to lower blocking probability.

This situation bears close resemblance to the situation in FDMA system in which Dy-

namic Channel Assignment (DCA) is applied. Various rearrangement methods of DCA

have been proposed previously [15]. In general, to obtain optimal solution for dynamic

channel assignment problem is NP-complete [16] and it is difficult to obtain optimal

rearrangement in a practical time. Hence, approximate methods must be applied for

remangemen t S.

Here, we propose a sub-optimal rearrangement method similar to the "1-celI Re-

mangement" method described in [15]. This method is based on a "First Level Re-

arrangement", in which only one code is allowed to rearrange. In contrat, a "t-Level

Rearrangement" ailows the donor to seize the spreading code from ânother donor and

this repeats iteratively for subsequent donors such that in total t different codes are rear-

ranged. In our proposed '1-cell Remangement", code is rearranged according to "First

Level Rearrangement" but we only arrange the code requiring only one donor. Hence,

we always prevent two or more donors from giving up the same code sirnultaneously

for the new user. With this scheme, the impairment upon the communication quality

due to remangements is reduced, since less arrangements imply that we disconnect less

calls (for a short time) during rearrangement. The load on the base station is also re-

duced. More important ly, it exhibits good performance compared to other sophisticated

schemes [15], while having low computational complexîty.

To implement this "1-cell Rearrangement" scheme, we adopt the methodology used

in distributed local-packing scheme proposed in [17]. The whole process calls for the

modification of the code allocation table, in order to include the necessary information

for the base station to make the reassignment decision.

table is shown in Table 3.2? Note that an extra column is

3the column containing the locations is not shown for simplicity

49

The modified code allocation

added to indicate the number

Code 1 user 1 1 1 2

1 I

new user i 1 16 1 1

n b l e 3.2: The code allocation table (modified)

of amilable codes for different users. This column is required to identify any possible

donors who have extra available codes to be assigned. Instead of marking with an X,

the user(s) which uses the corrcsponding code is indicated in the entry. Now we rnodiSr

the algorithm 1-A as fdlows,

1. When a new call request arrives, Say i, information about its location is sent to

the base station and recorded by adding an extra row i to the CAT.

2. Use CAT to check whether the angular separation between the new user and other

active users is below the MRAS. If yes, proceed to 3. Otherwise, go to 7.

3. The codes currently used users in Mi axe restricted from use by the new user. For

each prohibited code k, inclicate the user number(s), which is currently employing

the code, in the corresponding entry (à, k). The remaining clear entries form a

list of the possible codes for the new c d . If no code is possible, proceed to 4.

Otherwise, go to 7.

4. In CAT, from the row i corresponding to the new user, identik potential donors

(Le. the set Mi) who appeas in row i. From these potentid donors, we select o d y

those who are using distinct codes. That is, if two or more users are currently

using the same code, they are not qualified as donors.

5. For each donor candidate and fiom its corresponding row in CAT, if the number

in the "fke code" column, is greater than 0, the candidate becomes the final donor

for the new user. When more than one final donor exist, we randomly select one.

If no donor is available, the new call is blocked. Otherwise, proceed to 6.

6. The new user wiU use the code currently used by the donor, while the donor will

switch to any one of its amilable codes, as shown in CAT. Also, delete the donor's

old record in CAT. Go to 8.

7. For each possible code, find out fiom CAT which users are currently using it. The

new c d would select the code which is currently used by the nearest terminal.

Proceed to 8.

8. As the new user i is assigned a code j , update CAT by indicating user i in (i, j )

and (h, j ) for al1 h E hli. Update the "free coden column for these h. Repeat this

step for the donor (if any) with its now assigned code.

Note that in step 4, we only choose the users who are using distinct codes as

possible donors, according to the principle of "1-ce11 Rearrangement " . We now use an example to illustrate the above code rearrangement scheme. Assume

there are six users in one cell and their angular positions are as follows,

user numbcr 3 6 5 2 4

Figure 3.1: Users distribution

Thus, t hey are unifonnly distributed wit h angular separation between two adjacent

users equal to 12'. Let MRAS be 30' so that any user and its four neaxest users on two

sides cannot employ the same code. Now, refer to figure 3.2 for the evolution of CAT

as users arrive one by one. In (a), user 1 arrives and take any code (code 1 is chosen,

without loss of generality). In (b), user 2 arrives, and since it cannot use the same code

that user 1 is using(user 1 lies within the MRAS of user 2), it can take code number 2 or

3 (code 2 is chosen). And the CAT is updated accordingly, with their numbers appearing

in the correspondhg entries. Note that the number of kee codes must also be updated.

In (c), user 3 arrives and the only restriction is that it cannot use the code which mer

1 is using. User 2 imposes no restriction, since it is outside the MRAS of user 3. User 3

then choose code 2, according to the criterion in step 7. Then, user 4 arrives and so on.

Findy, when user 6 arrives, as shown in (f), there is no code available. The potential

donors can be user 1, 3, and 5. However, only user 3 will be the donor, since it has 1

fiee code and the others have none. Hence, user 3 now switches to code 3 from code 2,

as shown in (g). And user 6 seizes the code 2. The CAT is updated for the new status

of both users 3, 6 and the affected users within their M M S . Thus, code remangement

prevents user 6 from being blocked.

3.2 Simulation Mode1

The assumptions used in the last chapter are applied in the simulation model. The same

cellular environment consisting of a centre cell and two tiers of interfering cell (figure

2.2) is used. N subscribers are placed et random positions over each cell. N ranges from

25 to 1500. Fading and shadowing are not present. The signal attenuation follows the

simple path loss mode1 (2.13).

For reverse link, perfect power control is assumed and the received power at the

base station from individual subscriber is equal to 1. The SINR for subscriber i in the

center cell was calculated occording to the following equation,

where Pk, is the power received at center cell base station from subscriber m in cell k,

Gp is the processing gain and y is given in (2.16). The average SINR for aJl subscribers

within the centre ce11 are then obtained.

For forwaxd lid*, no power allocation or pilot signal is considered in order to

s i m p w the simulations process. We assume that the base station transmits equal

amount of power to each subscriber. The SIN& is given by,

1 (a) dter user 1 arrives

/' (b) after user 2 arrives

1 Code

(c) &ter user 3 arrives

1 Code 1

(e) after user 5 arrives

1 Code 1

(d) &ter user 4 arrives

/ (f) dter user 6 arrives. no code cm ôe assigned for user 6

lusu

(g) after code remangement by user 3, code is allocrited for user 6 succcssful~y.

Figure 3.2: Example of code allocation and rearrangement

Code

1 1 2 1 3 mm1

where Pkm is the power received by subscriber i from base station k due to subscriber

When using orthogonal transmission and adap tive base station antennas, the allo-

cation scheme will assign codes to N subscribers in random order, after N subsnibers

are placed in each cell. This implies that codes are assigned on a first corne k s t serve

basis, but the order of arriva1 of calls is random. The number of orthogonal codes is equal

to the processing gain which is assumed to be 128. For non-orthogonal transmission or

fked base station antennas, code allocation scheme is not necessary and not considered.

Antenna Patterns

We consider 5 different antenna configurations in the simulations:

antenna pattern 1

Table 3.3: Various antenna confaqu~utions used in the simvlutions

The radiation patterns of the omni-directional, "pie" and "parabolan antenna are

identical to those in the last chapter (figure 2.3-2.5). "Fiued" simply means that the

antenna pattern remains unchanged while "adaptive" allows the antenna pattern to be

steered towards the direction of the desired subscriber or base station. For both adaptive

"pie" and "parabola" antema, we assume that the MRAS is 30'.

spreading codes orthogonal orthogonal orthogonal orthogonal

non-orthogonal

-

3.3 Results and Discussions

base station omni-arec tional fîxed "pie" shape

adaptive "pie" shape adaptive "parabola" shape adap t ive "parabola" shape

First we confine our focus on a lgof i th LA. The results for all configurations are sum-

marized in figure 3.3. Note that the x-axis shows the number of successful users with

codes assigned, not the number of users (N) requesting a call. There is a maximum

hard limit of users (indicated by limited right-end of the curve) that the system can

subscriber terminal fixed "pie" shape fixed "pie" shape fixed "pie" shape fixed "pie" shape fixed "pie" s hape

accept, since the capacity is limited by the number of available codes. Usen with no

codes assigned are considered dropped. Note that the hard limit of capaciky is differ-

ent for different configurations, excep t for the case with non-orthogonal reverse channel

which has no capacity hard limit. SINR comparison among various configurations is

drawn from the region in which aJl hard limits have not been attained. As illustrated

in figure 3.3, the "pasabola" adaptive antenna with orthogonal codes shows the most

promising SINR performance and maq-fold improvement over the same antenna with

non-ort hogonal codes or the omni-directional antenna with orthogonal codes. This shows

the benefit of utilizing both SDMA and orthogonal CDMA. The "pie" adaptive antenna

shows comparable performance to the "pie" fixed antenna (or 60° sectorization), as their

SINR curves overlap each other. In other words, it verifies the observation made in last

chapter that with same directivity and uniformly distributed users, adaptive antenna in

FWA does not produce significant SINR improvement over fked antenna. However, a

careful examination shows that with "pie" fixed antenna, systen reaches the code's hard

limit before it reaches unacceptable SINR level ( 6 dB in this case). Under this circum-

stance, the system is now code-limited instead of interference-limited. Thus, the main

benefit of using adaptive antenna over static antenna cornes from the use of dynamic

code assignment, which increases the maximum number of users allowed. As shown in

figure 3.3, the hard limit of adaptive antenna (R 1229 users) is 60% higher than that of

fixed antenna (768 users), therefore achieving higher spectral efficiency through the use

of SDMA with dynamic code allocation. To illustrate this fact further, figure 3.5 shows

how many users are assigned the same code within the same cell at one time instant

(snapshot), when "pie" adaptive antenna is used and the number of users is large. Note

that alI codes are reused more than 8 times and some even 11 times. In comparison,

codes axe reused at most 6 tMes (6 sectors/ceIl) for the "pie" k e d mtenna.

The average SINR calcdation does not show whether an individual user is able to

maintain the required S M . This suggests another figure of merit, the outage probabil-

ity, which is defined below,

Note that users with no code assigned, i.e. blocking, axe also included in outage calcu-

lation. Thus we assume that unacceptable SINR can be treated as blockhg and vice

versa. The results of all configurations are summaxized in figure 3.4. Now the x-axis

shows the number of users requesting calls. Table 3.4 summarizes the capacities of all

configurations when the required probability of outage must be less than 0.05. With the

same directivity, the "pien adaptive antenna with dynamic code allocation can achieve

1.43 times the capacity of the "pie" fixed antema system. Finally? the "parabola" adap

tive antenna, combining the advantages of orthogonal CDMA and SDMA, achieves the

lowest probability of outage. It is able to support 1270 users, whereas the "parabola"

adaptive antenna with non-orthogonal codes supports 220 users and oh-directional

with orthogonal codes supports 130 users. Findy, it is worth mentioning that at high

outage probability, the outage performance of "pie" adaptive antenna seems to be in-

ferior to its fixed counterpart. This is explained by the fact that as more users are

admittecl using dynamic code allocation, the resulting SINR is lower as compaxed to

system with fewer users using fked antennas. This problem can be alleviated by using

appropriate call-admission policies based upon curent SINR level, which is a subject of

further research.

For the forward link, the average SINR of a3l users in the center cell is calcu-

lated and the results are summarized in figure 3.6. The results for outage probability

and relative capacities are shown in figure 3.7 and table 3.4. Adaptive antenna also

shows many-fold increase in capacity. In general, sirnilar conclusions frorn the reverse

link analysis c m be drawn for the forward link. It was suggested in [21] that non-

orthogonal forward link has comparable performance to the orthogonal forward link

under severe multipath interference. But in FWA with s m d multipath interference,

orthogonal CDMA is usually more favorable. Therefore, the dynamic code allocation

must also be employed in the fonvard li& when adaptive antennas are used at the base

station. Note that the capacities of forward and reverse Iink are very close, which is

contrary to the usual assumption that only the reverse link is critical. In fact, while the

forward link generdy provides smaller SINR than the reverse link (see figure 3.3 and

3.6), both links are nonetheless limited by the number of spreading codes. It is found

Table 3.4: The muximum number of asers which can be supported at an ovtoge probability of 5% for variow configurations

Fornurd Izn k

Configuration

1 Code Allocation Schemes 1 Fixed Allocation 1 LA 1 1-B 1 Code Remangement 1

Reverse lin k

Table 3.5: The maximum nurnber of users .which can be supported at blocking probability of 5% for various code allocation schemes

that the first 5% outage is mainly due to blocking instead of failure to meet the required

SINR. Thus, the capacity of both links should be very close, when 5% outage probability

is applied.

Comparison wit h Analytical Results

Figure 3.8 illustrates bot h the simulation results and analytical results obtained from

last chapter for capacity in reverse link. It is clear that simulations results are in good

agreement with the analytical results. The only difference is that the analytical analysis

assumes infinite number of orthogonal codes available, as its performance curve has no

hmd limit.

Comparison between Code Allocation Algorithms

We now focus on the performance improvement in terms of increasing the number of

codes s u c c e s s ~ y allocated, i.e. blocking probability. Figure 3.9 cornpaxes the perfor-

mance in terms of blocking probability between algorithms 1-A, 1-B, and conventional

six-sectors fixed allocation. For a fair cornparison, "pie" adaptive antema is used in

both 1-A and 1-B. Table 3.5 contains the simulations results for capacities found at 5%

blocking probability. 1-A exhibits a significant improvement over 1-B and fixed alloca-

tion in terms of the number of users successfully allocated with spreading codes. At

Pr(b1ocking) = 0.05,I-A provides 13% and 60% increase in capacity over 1-B and fixed

docation, respectively. It implies that minimizing the angular separation between two

users using the same code dows more "space" for other users to enter the system.

Figure 3.9 also shows the outage performance for code remangement scheme. In corn-

parison with the cases of no code rearrangement (1-A and 1-B), the blocking probability

decreases slightly The slight improvement reveals that code remangement can increase

the spectral efficiency by saving blocked calls. However, once the system is heavily

loaded, the improvement becomes less significant, since there are less qualified donors

who have available free codes for rearrangement.

As a brief summary of this chapter, we showed that using highly directive antenna

combined with orthogonal CDMA provides excellent SINR performance. For this reason,

the capacity is bounded by the number of codes rather than the required SINR. Also, the

benefit of using adaptive antennas for homogeneous traffic is not obvious, since static

antenna with sarne directivity can attain the same SINR improvement. However, by

using adaptive antennas combined with the proposed dynamic code allocation schemes,

we were able to generate more codes for users and hence the capacity is increased sig-

nificantly.

config. 1 +--t config. 2

. . O - confii.3 . - confg. 4 config. 5

500 Io00 number of successful users

Figure 3.3: SINR performance of mious configurations for reverse link

Figure 3.4: Outage probability of wious configurations for reverse link

Reverse link, outage probability

....................

p . . . . . . . . . . ' ' ' ' ' -1' ' ' ' *-4 config, 3

/

--.- .* 1

. . . . . . . . . . . . . . ..,. ...................

............ . . . . a . . , . . .

.............

10" O 500 Io00 1500

uses regutMing cal

1 .....+....................t-i...............................

I .l. ; ..................

I

i I u

n

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

r ....... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

i

I

........................... I I

A . . ...................... I I

I

code histogram I I 1 1 1 1

60 80 code number

Figure 3.5: Code histogram

U config. 1 ; . .... H config. 2 . . .:. - config. 3 1 I config.4 : L:

200 400 600 800 1000 number of successful users

Figure 3.6: SINR pedonnance of mious configurations for forward link

n Forward link, outage probability

Figure 3.7: Outage probability of various configurations for forward link

- - - (a)

X (b)

x (c) . -

0 0 (dl

200 400 600 800 1000 1200 1400 number of successful users

Figure 3.8 : Cornparison bet ween simulations and analytical results: (a) andy sis: adap tive "pie" ? (b) analysis: adaptive "parabola" , ( c ) simulation: adaptive Upien and (d) simulation: adap tive "parabola"

Figure 3.9: Cornparison of algorithm 1-A, 1-B and code-remangement

Chapter 4

Performance Enhancement using

orthogonal CDMA system wit h

Overlapping Sectors

In last chapter, it was shown that good SINR performance is not adequate in providing

high capacity due to the code limit. Using adaptive antema with the proposed code

allocation schemes is an effective way to expand the code limit or increase the code

reusability. However, the haxdware cost and cornplexity associated with adaptive an-

tenna array is one major constraining factor when SDMA is to be implemented in FWA.

Before, it was assumed that each subscriber is served by one set of adaptive processing

unit inside the base station antenna, so that an optimum radiation pattern is generated

for each subscriber. Hence, the cornplexity grows with the number of subscriber, which

is highly undesired. From a service provider's perspective, it is important to maintain

the cos t Jequipment reasonable, since most people justify a telephone by the cos t t hey

can fiord. Although adaptive antenna m a y is an attractive technique, it raises a sig-

nificant cost issue. One alternative is to use highly directional antenna (i.e. nanow fixed

sectorization) to achieve high spectral efficiency, as in 1s-95 in which t k e e 120 degrees

sectors are used to provide almost 3 tîmes gain in capacity. Nonetheless, limitation of the

beamwidth translates into only limited number of sectors and thereby an upper bound

of code reusability. To increase the code reusability without using adaptive antennas, we

propose the concept of overlapping &ed sectors or overlapping antenna beams, only in

this way c m we increase the number of sectors without utilizing a n t e ~ a patterns with

small beamwidth. As we will show in this chapter, overlapping fixed sectors cas provide

significant capacity improvement by dynamic code docation while not using adaptive

antennas.

4.1 System Mode1

Instead of adjusting the radiation pattern of antenna array adaptively, the antenna is

designed to radiate fixed and discrete beam patterns over different directions. Each

direction is characterized by the corresponding steering vector. This will reduce the

cost associated with complex signal processing hardware. We proceed by assuming that

there are S fked directional base station antennas serving a single cel1. By removing the

restriction that their radiation patterns should be disjoint (non-overlapping) in the an-

gular domain, we c m have more directional antennas, or more secton, thân conventional

system by having their radiation patterns overlapping each other, as depicted in figure

4.1. Given a radiation pattern common to ail antennas, we can control two parameters

in the proposed system: (1) the MRAS, (2) the angular distance between the centres

of two adjacent radiation patterns. The MRAS depends on the antenna pattern and is

equal to 30' in our analysis. We assume that these S overlapping sectors are uniformly

distributed, i.e. S fixed directional antennas with radiation patterns shown in figure 2.4.

The angular separation between the centres of two adjacent sectors is equal to 360'1s.

It is important to note that a user simultaneously lies within the coverage range of more

than one directional antenna or sector, and we let the number of these "co-sectonn be

2. Note that out of these Z CO-sectors, a user only selects one sector to operate with. Z

therefore is a function of M U S and S. For example, if S is equal to 36, the separation

between two adjacent sectors is 10' and Z is equal to 6. For simplicity, we name each

antenna as sector 1, 2 and etc, as illustrated in figure 4.1.

Figure 4.1: Example of overlapping sectors in a cell

4.2 Dynamic Spreading Code Assignment Algorithms

- Class II

By replacing dynamic sectors with f i e d sectors, class 1 algorithms are no longer valid.

In this section, new algorithms are proposed to increase code reusability. We defme two

code allocation tables, namely CAT-A and CAT-B. CAT-A indicates a l l the currently

active users with their operating sectors (sectors they choose to operate with) and codes

they use: as show in Table 4.1. When a new cal1 arrives, it uses CAT-A to check if

there is any active user who operates within any of its Z co-sectors. Those users will

receive the interference caused by the new user who is located in their operating sectors.

Hence, their codes are restricted from use by the new user. CAT-B also shows a l l the

currently active users and the codes they are using. But CAT-B differs from CAT-A

in that this information is recorded in a l l Z cesectors of active users, not just their

operating sectors, as shown in Table 4.2. CAT-B is needed because the signal fiom an

active user will be received by anyone who Lies within its Z CO-sectors, even though only

one of thern is the operating sector. When a new user arrives, it checks CAT-B to see if

it lies within the Z CO-sectors of âny active user. If this is tme, the new user cannot use

their codes.

1 Code 1

Table 4.1: The code allocation table A

1 Code 1

Table 4.2: The code allocation table B

iVe now use an example to illustrate the above idea and the underlying principle

of our proposed code assignment schemes.

Let Z equals to 3. Assuming that a new user arrives and its dosest sector is 3 and

CO-sectors are 2 and 4. The current CAT-A and CAT-B are shown as Table 4.1 and 4.2,

respectively. For simplici% we assume that only fùst five codes are used, i.e. first five

columns of Table 4.1 and 4.2 are considered. A code is a d a b l e o d y when both Tables

jointly show that the new uses do not interfere with existing users and vice versa when

using this code.

The new user is now faced with two choices during c d setup: the selection of sector

and code. In fact, the assignment schemes should consider all CO-sectors, such that if

one co-sector contains no amilable codes, the others can still be examined. The issue is

therefore the order of preference among these co-sectors. We propose two different code

allocation algorithms (II-A) and (II-B) which differ in the criteria for selecting sector. In

algorithm II-A, the sector selection criterion is to minimize the angular distance between

the centre of the chosen sector and the user. In order words, the new user examines

these CO-sectors in an order of increasing distance between the CO-sector and itself. This

is particularly important, since for common antenna patterns, the maximum is located

near the centre. Hence, the received power of the desired signal increases as the user

is closer to the centre of a sector. In algorithm II-B, the sector selection criterion is to

maintain a balance of the number of users in each sector or "water-filling" the aMilable

CO-sectors. This is achieved by choosing a sector which currently contains the rnost

available codes. Hence, no single sector will be crowded with too mmy users, while the

others axe empty.

Now, returning to the example. The new user starts by choosing sector 3 according

to ILA. From C.4T-A and the three rows corresponding to the CO-sectors 2,3,4, it is found

that code 2 and 3 are two candidate codes. since (2,2), (2,3), (3,2), (3,3), (4,2) and (4,3)

are clear. Next, row 3 of CAT-B is examined, since the new user m t s to operate in

sector 3. By looking at (3,2) and (3,3) in CAT-B (corresponding to code 2 and 3),

only code 2 is the final choice, as using code 3 will result in receiving interference from

user 4. On the other hand, if no code is available, the above procedure is repeated for

next CO-sector of the new user. The new user is blocked when aJl CO-sectors have been

examined,

The new code allocation algorithm (II-A) is stated as follows,

1. When a new call request arrives, information about its location is sent to the base

station. Let the new user's CO-sectors be Si, Sz, ... Sz

2. Select the new user's nearest sector Si which is not selected yet. In CAT-A, find

any column k which has cleax entries for aU rows SI, S2, ... SZ. In CAT-B, for each

k obtained above, check if the entry (Si, k) is a clear entry. If so, code k is available

and proceed to step 3. When no code is available, repeat this step with the next

nearest sector. If a l l Z CO-sectors had been examined and no code is available, the

c d is blocked.

3. For each candidate code k, find out from CAT-A which users are currently using

it. The new c d would select the code which is currently used by the nearest user's

terminal.

4. Update CAT-B by indicating the new user in the entry corresponding to the chosen

code for a l l its Z CO-sectors, i.e. (Si, k), (Sz, k), ..., (Sz, k). Update CAT-A in the

same way but only in the row corresponding to the chosen sector, Le. (Si, k).

To illustrate the usefulness of employing two code allocation tables, an example is

presented in figure 4.2. The figure clearly indicates that user A lies within the coverage

Denote the sector with solid boundiiry by s 1.

Denote the sector with dash boundary by s2

Figure 4.2: Example of using CAT-A and CAT-B

of sector S2 but not Si, while user B lies in both. We now examine two scenarios.

In the first scenario, B is an active user using code #1 in Si while A is the new

user seeking for an available code. The question is whether A c m reuse the same code

#1 in S2. First, CAT-A is used to show whether signal fiom A would interfere with

existing user B. Clearly, B operates in Si, which is not the CO-sectors of A. Thus, the

interference caused by A would not be received by user B. Next, CAT-B is used to check

the opposite situation, which is whether the signal from B would interfere user A. This

is an alternative to ask whether user A lies in any CO-sectors of B. n o m CAT-B, S2 is

one of the CO-sectors of B. Thus, A should not use the same code #l.

In the second scenario, the situation is reverse. A is already in the system while B

is the new user. By following the same procedure, we find that CAT-A indicates that A

would receive the interference fiom B, but not vice versa if SI is selected for B. However,

since both conditions from C.4T-A and CAT-B must be met to d o w code-reuse, we

conclude that B should not use the same code as A uses.

Next , aaother algorithm II-B, which is a slight variation of algorithm 1-B, is pro-

posed. We rnodiSr step 2 of II-A as follows,

2. For each CO-sector, check CAT-A and CAT-B to see which code is available for use

by following the same procedure as in step 2 of II-A. The CO-sector Si corresponds

to greatest number of available codes will be selected. If there is no available code

for al1 Z CO-sectors, the cal1 is blocked.

4.2.1 Code Rearrangement

Like the class 1 algorithms, we d o w code rearrangements to be combined with class

II algorithms. Since a user has the flexibility to choose from more than one sector to

operate with, the code rearrangement scheme can be extended to switching sector in

addition to switching code. We also utilize two code allocation tables (CAS-A and

CAT-B) to make code rearrangement decisions. The rearrangement scheme, based on

II-A and "1-cell Remangement", is stated as follows,

1. When a new c d request arrives, information about its location is sent to the base

station. Let the new user's CO-sectors be SI, &, ... Sz

2. Select the new user's nearest sector Si which is not selected yet. In CAT-A, find

any column k which has clear entries in all rows Si, S2, . .. Sz. In CAT-B , for each k

obtained above, check if the entry (Si, k) is a clear entry. If so, code k is adab le

and proceed to step 5. When no code is available, proceed to step 3. If a l l Z

CO-sectors had been examined and no code is available, the c d is blocked.

3. From the chosen sector Si, look for possible donors, who prohibit the code from

being used by the new c d , i.e. users appear in rows SI, S2, ..., Sz of CAT-A

orland row Si of CAT-B. From all possible donors, we only select those who are

using distinct codes and have available free codesL for themselves to switch to. If

there are more than one such donor, priority is given to the one who does not

require switching sectors. Randomly select one if ail donors have same priority.

Findy, if there is a donor found, proceed to 4. Otherwise, repeat step 2 with the

next neares t sec tor .

4. The new user will use the code currently used by the donor, while the donor will

switch to any one of its available codes orland sectors. Clear the previous record

for the donor in both CAT-A and CAT-B. Go to 6

5. For each candidate code, find out from CAT-A which users are cwently using it.

The new call would select the code which is cunently used by the nearest user's

terminai. Proceed to 6.

6. Update CAT-B by indicating the new user in the entry corresponding to the chosen

code k for all its Z CO-sectors, i.e. (Si, k), (S2, k), ..., (Sz, k). Update CAT-A in the

same way but only in the row corresponding to the chosen sector, i.e. (Si, k ) If

donor exists, repeat this step for donor with its new operating sector orland code.

4.3 Results and Discussions

Again, we confine our discussions to reverse link in this section. The simulation mode1

is the same as that for class 1 algorithms, with the exception that both the base station

and the subscriber must be using the same 60' "pien aatenna (see figure 2.4). Only one

ISince no free code column is used in CAT, we should follow the procedure in step 2 to find avaiiable fiee codes for each possible donor

73

type of a n t e ~ a is considered because our focus is not on the performance of different

antemas but the proposed overlapping secton system. The MRAS is 30'. Also, we

rneasure the performance by blocking probability. Therefore, the failure of not attain-

ing the required SINR is not considered and we assess the performance simply by how

many spreading codes are offered per area. The reason is that the target value of SINR

can be improved (lowered) by other communication techniques such as better coding or

modulation schemes, and it is not the main objective of using overlapping sectors. We

first investigate the performance of algorithm II-A and II-B, as shown in figure 4.3. II-B

provides slightly better performance than II-A. Thus, the appropriate criteria in assign-

ing sector is to evenly distribute the users to different sectors. It is also an alternative

way to average out the interference received by each user. However, this improvement

cornes at the expense of increasing the angular distance between the user and the center

of chosen sector, which may be undesirable for common radiation patterns. Next we

investigate the effect of number of overlapping sectors on the performance of system.

Figure 4.4 illustrâtes the blocking probability when the number of sectors equals to 18

and 36. As the number of sectors increases, more users are allocated codes ~uccessfully,

at the expense of increasing hardware complexity. Another important observation is that

the adap tive antenna m a y (using algorit hm LA) provides the lowest blockng proba-

bility. This is reasonable in view of the fact that using adaptive antema is in fact an

alternative representation of many fixed sectors (i.e. dynamic sector produced for each

user). Nonet heless, both cases wit h st atic overlapping sectors outperform the conven-

tional 6-sectors system which can only accept 768 users. At 5% outage probability and

wi th code remangement, 18-sectors and 3 6-sectors increases the capaci ty over 6-sectors

by 16% and 45%, respectively, while adaptive antenna provides only 60% improvement.

Thus, if adaptive antennas cannot be employed due to limitation in hardware cost or

complexi ty, s t atic antennas providing overlapping sectors are good alternatives.

Figure 4.4 also illustrates the improvement of utilizing code rearrangement scheme

combined with class II algorithms. At Pr(outage) = 0.001 and in the case of Iû-sectors,

the improvement is significant as the number of users increases by about 14%. However,

a t higher blocking probability or more sectors are deployed, the improvement tends to

diminish as t r a c load becomes heavy. This can be explained by the fact that, number

of available codes for remangement drop rapidly as t r a c load increases and hence the

improvement becomes less significant.

l oo L.. . . . . . . . . . . . . . A . . . . . . . . . . . . . . . .!. . . . . . . . . . . . . .. S . . . . .. . . . . . . . . . .!.. . . - - . - - - - - . .. .,. . . . . . . . . . . . . . ..4

Figure 4.3: Cornparison between algorithm II-A and II-B

Figure 4.4: Cornparisons between overlapping sectors and dynamic sectors: (a) conven- tional 6-sectors, (b) l&sectors, (c) 18-sectors with remangement, (e) JGsectors, (e) 36sectors with rearrangement, (f) dynamic sector, (g) dynamic sector with rearrange- ment

Chapter 5

In t his t hesis, we study the capacity enhancement s of Fixed Wireless Access (FWA) sys-

tem, with the focus on the joint application of SDMA, orthogonal CDMA and dynamic

code allocation. SDMA is a means to spatially Mter or suppress signals ixrriving with

angles different from the desired signal, which can be realized by employing adaptive

a n t e ~ a array at the base station. The static nature of FWA radio channel dows both

SDMA and orthogonal CDMA system to be implemented effectively. Also, the use of di-

rectional antenna at subscriber site provides an additional enhancement by reducing the

undesired interference to users in other cells. In chapter 2, we compared and contrasted

the performance of SDMAICDMA and conventional CDMA systems, by investigating

wious configurations of base station and subscriber antenna combined with either or-

thogonal or non-orthogonal spreading codes. The capaci ty equations for both forward

and reverse link were analyticdy derived. In all cases, orthogonal CDMA outperfonns

its non-orthogonal counterparts. It was found that the performance of antenna is a

function of the directivity for homogeneous tr&c, regardless of whether the antenna is

adaptive or static. Although using both highly directive antenna and orthogonal CDMA

provides excellent SINR performance, the system is nonetheless limited by the number

of a d a b l e codes. This problem deteriorates if the required SINR becomes lower as

powerfùl coding and modulation schemes are employed. This is because the received

SINR might fax exceed the required SINR and the discrepancy between the capacities,

one imposed by the code limit and the other one by the required SINR, increases. That

is, more users are not admitted to the system not because of inadequate SINR but short-

age of codes. Moreover, if the antenna is adaptive, dynamic code allocation is required,

since the conventional fixed sectorization no longer applies. These reasons suggest the

development of new code allocation schemes, which are the topics in chapter 3. In chap-

ter 3, several code allocation schemes were proposed and analyzed by simulations. In

particular, the best scheme proposed is shown to provide 60% increase in the number of

users with code successhilly allocated, over the conventional fixed sectorization. And at

required SINR=6 dB, the capacity is improved by 40%. Note that at a lower required

SINR, the improvement should be able to reach 6O%, when all users are blocked due to

shortage of code rather than SINR. Next, another code allocation scheme was designed to

d o w code rearrangement among active users in the system, and hence is capable of sav-

ing blocked c d s . We therefore concluded that the main benefit of using SDMAICDMA

system cornes partly from the reduction of multiple access interference and partly from

the high reusability of spreading codes using the proposed code allocation schemes.

Motivated by the need of efficiently reusing the codes without suffering fiom the

hardware cost and complexi ty associated wit h adap t ive antennas, a new concept based

upon the overlapping sectors is introduced in chapter 4. Several code allocation schemes

designed for such system were proposed and simulations showed that improvement they

provide grows linearly with the number of sectors being deployed. In particular, a 36-

sectors system is able to improve the capacity over conventional iked allocation by 45%.

Although inferior to adaptive antennas, it outperforms fixed sectorization significantly.

Hence, it is a cost-effective alternative to implement dynamic code allocation in FWA

system.

5.1 Future Work

In this thesis, an ideal FWA radio channel had been assumed. A possible research

direction is to extend our results to non-ideal channel which exists in urban FWA en-

vironment. Since a signal is received via different multipaths, the optimal radiation

pattern of an adaptive antenna does not always require the maximum gain pointing at

the direction of the subscriber. Consequently, the MRAS varies with the condition of

radio Channel and the amount of multipath interference fiom other users.

Another possible research direction is to study the effect of non-ideal antenna

patterns. The relationship between the MRAS and the sidelobe gain must be carefùlly

exarnined. Otherwise, once the interference fiom an undesired user, who uses the same

orthogonal code, is received via the sidelobes, it will cause severe degradation to the

SINR. But for non-orthogonal codes, this will be a less significant issue because no user

will be using the same code in the system.

Finally, more powerful code allocation schemes can be devised at the expense of

higher complexity. More attention should be paid in this aspect, since highly efficient

coding or modulation schemes developed in the future will lower the required SINR and

intensify the problem of shortage of spreading code.

Appendix A

The op timd weight vector WOpt is derived sub ject to maximizing the signal-to-noise

ratio. The objective is to show that WOpt is a function of the desired signal's direction

of arriva1 (DOA), which can be easily obtained in WLL environment.

We assume that the number of users is large such that the MAI can be modelled

as additive Gaussian noise with zero mean and variance given by 1.7. Let nm be the

analytical signal associated with the noise signal received by the antennâ element m, the

noise includes a l l the undesired signals (MAI+background noise). Let us consider the

signal received at element m of the antenna array. It has a useful signal component s,

and a noise component nm,

U m = s m + ~ m ( A 4

The output signal

and a noise component r,,

has a useful component r,,

Using vectorial notation, the desired and noise components become,

Hence, the corresponding output signals c m rewritten as,

The average noise output power is

. Assuming that the variation rate of the weights is much below that of the noise, as in

WLL, then,

P, = W~E(N'N*)W = W ~ O W ( A M )

where @ = E ( N * N ~ ) is the covariance matrix of the noise components. Since @ is

Hermitian, a matrix A may be found such that

The existence of such a matrix dows us to treat a problem equivalent to the initial one,

but more simple. Let us consider the transformation of the above signals using A ,

and

Now, the output noise power is,

H H P,, = E(z,zC) = w A OAW = llWl12

82

where,

Then the SINR at the output is,

S I N R = E(z.z;)/P, = (A.18)

In order to rnaximize the SINR, it is sufEcient to choose the vector w to be collinear

with si, according to Cauchy-Schwartz inequality. The optimal value of w is,

where p is a scalar constant Retunllng to W,

It cm be shown (refer to [lS]) that A A ~ = 9-' It follows that,

Let the desired signal arriving with angle 9,

where 4di = d(m - 1) sine. Hence the WOpt is a function of the direction of arrid

(DOA).

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