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§ors, (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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