Robust Cross-Layer Scheduling Design in Multi-user Multi-antenna Wireless Systems

Abstract of thesis entitled
Robust Cross-Layer Scheduling Design in Multi-user
Multi-antenna Wireless Systems
submitted by
Meilong Jiang
for the degree of Doctor of Philosophy
at The University of Hong Kong
in October 2006
Cross-layer design for a multi-user multi-antenna system has been shown to offer high spectral
efficiency which benefits from the inherent multi-user diversity and spatial multiplexing gain in
wireless fading channels. In this thesis, we consider the cross-layer scheduling design under var-
ious practical physical layer and network layer constraints for a wireless system with one base
station (with N antennas) and K mobile users (each with a single antenna).
In the first part of the thesis, we study the cross-layer scheduling design with imperfect channel
state information (CSI) at the base station for delay-tolerant applications. The CSI imperfectness
may derive from the CSI estimation error or the CSI outdatedness due to feedback and duplexing
delay. With imperfect CSI at transmitter (CSIT), there exists a potential packet transmission error
when the scheduled data rate exceeds the instantaneous channel capacity referring to packet out-
age. Our objective is to maximize the average system goodput, which measures the average b/s/Hz
delivered to the mobiles successfully. In practical wireless systems, a discrete set of rates instead
of an infinite continuous rate can only be supported due to the finite choice of error correction
encoders and discrete level constellations. To this end, the cross-layer design is formulated as a
mixed convex and combinatorial optimization problem, with respect to the imperfect CSIT statis-
tics and the discrete rate set constraint.
In the second part, we extend the scheduling design for the heterogeneous user applications such as
voice and data services. To take delay sensitive users into consideration, we employ both queueing
theory and information theory to model the system dynamics. A novel cross-layer heterogeneous
scheduler is designed to exploit the spatial multiplexing gain as well as the multi-user selection
diversity gain while also maintaining the delay constraints of the delay sensitive users.
Numerical results and comparison with various start-of-art scheduling schemes are provided to
demonstrate the potential of our proposed schemes. Specifically, by considering the CSIT error

statistics, source statistics and queueing delay into the design, the proposed scheduling schemes
provide significant performance enhancement in terms of system goodput, robustness with respect
to imperfect CSIT, and quality of service (QoS) guarantees.
Robust Cross-Layer Scheduling Design in Multi-user
Multi-antenna Wireless Systems
by
Meilong Jiang
MSEE, Beijing University of Posts and Telecomms.
A thesis submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
Electrical and Electronic Engineering
at
The University of Hong Kong
in October 2006
Copyright
c
 2006 Meilong Jiang
i
Declaration
I declare that this thesis represents my own work, except where due acknowledgement is made,
and that it has not been previously included in a thesis, dissertation or report submitted to this
University or to any other institution for a degree diploma or other qualifications.
Signed
Meilong Jiang
ii
Acknowledgments
I AM DEEPLY INDEBTED to my supervisor, Prof. Vincent K.N. Lau for having invariably
given me his patient guidance, stimulating encouragement, and deep insights into my research and
my life as well. His enthusiastic attitude and extremely high efficiency has not only had a great

impact on my Ph.D study, but has also given me great impetus that I would be able to cherish in
my entire life. The completion of this thesis would not have been possible without his continual
support.
I am sincerely grateful to the Graduate School of HKU for having provided the Postgraduate
Studentship during the whole Ph.D program. I would like to thank Dr. N. Wong, Prof. J. Wang,
Dr. W.H Lam, and Prof. Roger Cheng for their insightful guidance, suggestions, and kind help
during my study. I would also like to thank Prof. Ricky Kwok, Prof. K.L Ho, and Prof. Li chun
Wang for serving on my thesis examination committee.
I truly appreciate the friendship of my friends for having created a pleasant working environ-
ment and for their helpful discussions. Special thanks go to Mr. Tyrone Kwok, Mr. Gan Zheng,
Mr. Carson Hung, Mr. David Hui, Doctors-to-be- Xiaoshan Liu , Guanghua Yang and Shaodan
Ma, Dr. Zhifeng Diao, Dr. Xiaohui Lin, and Dr. Yiqing Zhou for their kind help and insightful
discussions. Many thanks go to other friends in the lab and research group.
Finally, I would like to express my sincerest gratitude to my parents and my wife Ying Zheng
for their deepest love and constant support.
iii
Table of Contents
Page
Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
Notation and used symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Evolution and Challenge of Wireless Technology . . . . . . . . . . . . . . . . . . 1
1.2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Motivation and Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Thesis Research and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1 Wireless Fading Channel - Characterizations and Mitigation . . . . . . . . . . . . 8
2.1.1 Large-Scale Fading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.2 Small-Scale Fading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.3 Mitigation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Cross-Layer Scheduling and Adaptive Design in Multi-user Wireless Network . . 13
2.2.1 Adaptive Design in Physical Layer . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 MAC Layer Scheduling Model . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Linear Transmit-receive Processing in Multi-antenna Base Station . . . . . . . . . 17
2.3.1 Zero-forcing Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.2 Transmit MMSE Processing . . . . . . . . . . . . . . . . . . . . . . . . . 21
iv
Page
3 Uplink Scheduling Design with Outdated CSI . . . . . . . . . . . . . . . . . . . . . 22
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Multi-user SIMO System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.1 Channel Model with Outdated CSIT . . . . . . . . . . . . . . . . . . . . 23
3.2.2 Multi-user Uplink Physical Layer Model . . . . . . . . . . . . . . . . . . 23
3.2.3 Packet Outage Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Uplink Space Time Scheduling Design . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.1 System Utility Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.2 Optimal Solution with Perfect CSI . . . . . . . . . . . . . . . . . . . . . 27
3.3.3 Heuristic Solution with Perfect CSI - Genetic Algorithm . . . . . . . . . . 27
3.4 Scheduling with Outdated CSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4.1 Performance Degradation of Ideal Schedulers Due to Outdated CSI . . . . 28
3.4.2 Proposed Scheme A - Rate Quantization . . . . . . . . . . . . . . . . . . 31
3.4.3 Proposed Scheme B - Rate Discounting . . . . . . . . . . . . . . . . . . . 31
3.5 Numerical Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 32
4 Cross-Layer Downlink Scheduling and Rate Quantization Design with Imperfect
CSIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2 Multi-user MISO System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.1 Downlink Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.2 Imperfect CSIT Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2.3 Multi-user Downlink Physical Layer Model . . . . . . . . . . . . . . . . 40
4.3 Problem Formulation of Cross-Layer Scheduling . . . . . . . . . . . . . . . . . . 41
4.3.1 Instantaneous Channel Capacity and System Goodput . . . . . . . . . . . 43
4.3.2 Cross-Layer Design Optimization . . . . . . . . . . . . . . . . . . . . . . 44
4.4 Solutions of the Optimization Designs . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4.1 Combined Scheduling and Rate Quantization Optimization . . . . . . . . 45
4.4.2 Optimal Inner Scheduling Based on Imperfect CSIT . . . . . . . . . . . . 47
4.4.3 Optimal Transmission Modes Design . . . . . . . . . . . . . . . . . . . . 50
4.4.4 Summary of the Scheduler Solution . . . . . . . . . . . . . . . . . . . . . 52
4.5 Numerical Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.5.1 Performance of Regular Scheduler with Imperfect CSIT . . . . . . . . . . 54
4.5.2 Performance of Proposed Scheduler with Imperfect CSIT . . . . . . . . . 54
5 Performance Analysis of Downlink Scheduling for Voice and Data Applications . . 65
5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
v
Page
5.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.2.1 Channel model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2.2 Multi-user Physical Layer Model . . . . . . . . . . . . . . . . . . . . . . 67
5.2.3 Source Model - Voice and Data . . . . . . . . . . . . . . . . . . . . . . . 69
5.3 Space Time Scheduling for Heterogeneous Users . . . . . . . . . . . . . . . . . . 69
5.3.1 Asymptotic Spatial Multiplexing Gain . . . . . . . . . . . . . . . . . . . 69
5.3.2 Scheduling Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.4 Numerical Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.4.1 Delay Performance of VoIP users . . . . . . . . . . . . . . . . . . . . . . 78
5.4.2 Spatial Multiplexing Gains on System Capacity . . . . . . . . . . . . . . 78

5.4.3 Transient Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6 Cross-layer Downlink Scheduling with Heterogeneous Delay Constraints . . . . . . 84
6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.2.1 Multi-user Physical Layer Model . . . . . . . . . . . . . . . . . . . . . . 85
6.2.2 Source Model - Delay Sensitive and Delay Insensitive . . . . . . . . . . . 86
6.3 Formulation of the Cross-layer Design for Heterogeneous Users . . . . . . . . . . 87
6.4 Solution of the Cross-Layer Optimization Problem . . . . . . . . . . . . . . . . . 90
6.4.1 Convex Optimization on (p
1
, .., p
K
) . . . . . . . . . . . . . . . . . . . . . 90
6.4.2 Combinatorial Search on Admissible Set . . . . . . . . . . . . . . . . . . 91
6.5 Numerical Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.5.1 Delay Performance of the Proposed Scheduler . . . . . . . . . . . . . . . 91
6.5.2 System Throughput Performance . . . . . . . . . . . . . . . . . . . . . . 93
6.5.3 Delay and Power Tradeoff . . . . . . . . . . . . . . . . . . . . . . . . . . 93
7 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
List of References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
vi
List of Tables
Table Page
3.1 Optimal quantization levels with respect to CSI error variance. . . . . . . . . . . . . . 31
4.1 Design example of rate partition, rate centroid and modes for CSIT error σ
2
= 0.1,
Q = 8, SNR=10dB and target frame error rate (FER)  = 0.01. . . . . . . . . . . . . . 52

vii
List of Figures
Figure Page
2.1 Wireless fading channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 System diagram for the cross-layer design framework . . . . . . . . . . . . . . . . . . 14
2.3 Block diagrams of the MAC layer scheduling . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Linear downlink transmit strategy with isolated encoding and beamforming. . . . . . . 19
3.1 Block diagram of the zero-forcing MUD (multi-user detection) at the base station with
n
R
receive antennas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Performance degradation of naive cross-layer schedulers (designed for perfect CSI)
with outdated CSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3 Outage probability of naive scheduler versus various CSI error and SNR . . . . . . . . 30
3.4 System throughput (b/s/Hz successfully received) versus rate discounting factor where
SNR = 6dB, n
R
=2, σ
2
= 0.05 ∼ 0.1. . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.5 Performance of maximal throughput schedulers of perfect CSIT, rate quantization and
rate discounting with outdated CSIT, ideal (naive) scheduler with outdated CSIT . . . 34
3.6 Illustration of crossover operation in Genetic algorithm . . . . . . . . . . . . . . . . . 36
4.1 One cell system model of multi-user MISO system . . . . . . . . . . . . . . . . . . . 39
4.2 Multi-antenna base station architecture with linear transmit processing . . . . . . . . . 42
4.3 Block diagram of the cross-layer scheduling algorithm. . . . . . . . . . . . . . . . . . 46
4.4 Performance degradation of naive scheduler (scheduler designed for perfect CSIT),
no quantization (Q = ∞): system goodput versus SNR in the presence of imperfect
CSIT for n
T

= 4 and K = 10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
viii
Figure Page
4.5 Average system goodput versus SNR for the naive scheduler (designed for perfect
CSIT) with rate quantization (Q = 8) at imperfect CSIT cases, n
T
= 4. . . . . . . . . 56
4.6 Packet outage probability versus CSIT errors of the naive scheduler (with no quantiza-
tion), the naive scheduler (with rate quantization Q = 8) and the proposed scheduler
(with rate quantization Q = 8) for n
T
= 2. . . . . . . . . . . . . . . . . . . . . . . . 57
4.7 Performance comparison of the proposed scheduler, the naive scheduler and the round
robin scheduler with CSIT error σ
2
= 0.05 at n
T
= 2, 4, 6. . . . . . . . . . . . . . . . 59
4.8 Sensitivity of average system goodput to the CSIT errors for the naive scheduler, the
proposed scheduler and the round robin scheduler at n
T
= 4 and Q = 8. . . . . . . . . 60
4.9 Average system goodput versus number of users for proposed scheduler, RXZF sched-
uler, TDMA scheduler and opportunistic beamforming scheduler under outdated CSIT
with speed= [20, 60] km/h and CSIT delay τ = 300µs (with equivalent error variance
σ
2
=[0.015, 0.13] for 5GHz carrier frequency); Transmit antenna n
T
= 4, receiver

antenna for each user n
R
= 1 (except for linear RXZF scheduler n
T
= n
R
= 4);
SNR = 15dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.1 System model of a multi-user wireless system with a base station (n
T
transmit anten-
nas), K
voice
voice client users and K
data
data client users. . . . . . . . . . . . . . . . . 66
5.2 Scheduling and queueing model for voice and data users . . . . . . . . . . . . . . . . 70
5.3 Overall space time scheduling algorithm. . . . . . . . . . . . . . . . . . . . . . . . . 73
5.4 Scheduling algorithm of the voice and data space time scheduler. . . . . . . . . . . . . 75
5.5 Delay performance of VoIP users with background data traffic (BW = 20kHz, n
T
=4,
K
data
=20, K
voice
=2, T
0
= 20ms). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.6 Spatial multiplexing gains of voice and data users (BW = 20kHz, K

data
=20, K
voice
=2, T
0
= 20ms). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.7 Transient performance of voice users in the presence of bursty data loading. BW =
20kHz, n
T
=4, K
data
=20, K
voice
=2, T
0
= 20ms. . . . . . . . . . . . . . . . . . . . . 82
6.1 Queueing model and scheduling model . . . . . . . . . . . . . . . . . . . . . . . . . 87
ix
Figure Page
6.2 Mean packet delay (number of time slots) of two class users versus background data
traffic λ (packets/time slot) (BW = 20kHz, n
T
=4, t
s
= 2ms) . . . . . . . . . . . . . . 92
6.3 Comparison of optimal, heuristic heterogeneous schedulers and Round-Robin sched-
uler on Spatial multiplexing gains over n
T
(BW = 20kHz, K
1

= 8, K
2
= 8, t
s
=
2ms, SNR = 6 dB and 16 dB) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.4 Minimum transmission power versus first class users delay requirement . . . . . . . . 96
6.5 Waiting time model consisting of three parts . . . . . . . . . . . . . . . . . . . . . . 96
6.6 Flow chart of iterative lagrange multiplier algorithm. . . . . . . . . . . . . . . . . . . 101
x
Abstract
Cross-layer design for a multi-user multi-antenna system has been shown to offer high spec-
tral efficiency which benefits from the inherent multi-user diversity and spatial multiplexing gain
in wireless fading channels. In this thesis, we consider the cross-layer scheduling design under
various practical physical layer and network layer constraints for a wireless system with one base
station (with N antennas) and K mobile users (each with a single antenna).
In the first part of the thesis, we study the cross-layer scheduling design with imperfect channel
state information (CSI) at the base station for delay-tolerant applications. The CSI imperfectness
may derive from the CSI estimation error or the CSI outdatedness due to feedback and duplexing
delay. With imperfect CSI at transmitter (CSIT), there exists a potential packet transmission er-
ror when the scheduled data rate exceeds the instantaneous channel capacity referring to packet
outage. Our objective is to maximize the average system goodput, which measures the average
b/s/Hz delivered to the mobiles successfully. In practical wireless systems, a discrete set of rates
instead of an infinite continuous rate can only be supported due to the finite choice of error cor-
rection encoders and discrete level constellations. To this end, the cross-layer design is formulated
as a mixed convex and combinatorial optimization problem, with respect to the imperfect CSIT
statistics and the discrete rate set constraint.
In the second part, we extend the scheduling design for the heterogeneous user applications
such as voice and data services. To take delay sensitive users into consideration, we employ both
xi

queueing theory and information theory to model the system dynamics. A novel cross-layer het-
erogeneous scheduler is designed to exploit the spatial multiplexing gain as well as the multi-user
selection diversity gain while also maintaining the delay constraints of the delay sensitive users.
Numerical results and comparison with various start-of-art scheduling schemes are provided
to demonstrate the potential of our proposed schemes. Specifically, by considering the CSIT error
statistics, source statistics and queueing delay into the design, the proposed scheduling schemes
provide significant performance enhancement in terms of system goodput, robustness with respect
to imperfect CSIT, and quality of service (QoS) guarantees.
xii
Abbreviations
AMPS advanced mobile phone service
AWGN additive white Gaussian noise
B3G beyond third generation cellular system
BER bit error rate
BPSK binary phase shift keying
BSC base station controller
CBR constant bit rate
CDMA code division multiple access
CSCG circularly symmetric complex Gaussian
CSI channel state information
CSIR channel state information at receiver
CSIT channel state information at transmitter
D-AMPS digital advanced mobile phone service
D-BLAST diagonal Bell layered space-time
DFE decision feedback equalization
DFT discrete Fourier transform
DS/SS direct sequence spread spectrum
DVB digital video broadcasting
EV-DO evolution-data optimized
EV-DV evolution-data and video

FDD frequency division duplex
FER frame error rate
xiii
FFT fast Fourier transform
FH/SS frequency-hopping spread spectrum
FTP file transfer protocol
GA genetic algorithm
GSM global system for mobile communications
HSDPA high-speed downlink packet access
i.i.d. independent and identically distributed
IP internet protocol
ISI inter-symbol interference
LAN local area network
LDPC low-density parity-check code
LMS least mean square
LS least square
MAC media access control
MAN metropolitan area network
MC-CDMA multiple carrier-code division multiple access
MIMO multi-input multi-output
MISO multi-input single-output
MLSE maximum likelihood sequence estimation
MMSE minimum mean square error
NLOS non line of sight
OFDM orthogonal frequency division multiplexing
OFDMA orthogonal frequency division multiple access
pdf probability density function
PHY physical layer
PLL phase-locked-loop
PRMA packet-reservation-multiple-access

PSD power spectra density
xiv
PSK phase shift keying
QAM quadrature amplitude modulation
QoS quality of service
QPSK quadrature phase shift keying
RRS round robin scheduler
RXZF receiver zero-forcing
SDMA space division multiple access
SIC successive interference cancellation
SIMO single-input multi-output
SISO single-input single-output
SNR signal to noise power ratio
STBC space time block code
TCP transmission control protocol
TDD time division duplex
TDE time domain equalization
UDP user datagram protocol
UMTS universal mobile telecommunications system
UWB ultra-wideband
V-BLAST vertical Bell labs layered space-time
VoIP voice over IP
Wi-MAX worldwide interoperability for microwave access
WLAN wireless local area networks
WMAN wireless metropolitan area network
ZF zero forcing
ZFBF zero forcing beamforming
xv
Notation and used symbols
In this thesis, scalar variables are presented as plain lower-case letters, vectors as bold face

lower-case and matrices as bold-face upper cases letters. The following is the commonly used
notations and symbols in the thesis.
A
ij
The ij-th element of the matrix A.
A
∗
Complex conjugate transpose (Hermitian transpose) of the matrix A.
A
T
Transpose of the matrix A.
A
−1
Inverse of the matrix A.
diag(x
1
, ..., x
n
) A n × n diagonal matrix with diagonal elements {x
1
, ..., x
n
}.
tr(A) The trace of the matrix A, T r(A) =

i
A
ii
.
(x)

+
Is a short notation for max[0, x].
E[x] Statistical expectation of random variable x.
arg max
S
(V (S)) The maximizing argument of the function V (·) over the set S.
O(V) The value that is in the same order of V.
Pr[A < B] The probability of the event A < B.
||x|| The Euclidean norm of vector x.
|A| The cardinality of the active user set A.
F
χ
2
(y) The cdf of a chi-square random variable y.
1
Chapter 1
Introduction
1.1 Evolution and Challenge of Wireless Technology
The wireless industry has witnessed its explosive growth since the first generation cellular
network was launched in 1979. For example, the first generation analog cellular system (AMPS)
has given way to 2G cellular systems (such as GSM, D-AMPS, and IS-95), and nowadays, we
have 3G systems (CDMA2000, UMTS), 3.5G systems (HSDPA,EV-DO,EV-DV), B3G systems
(Beyond 3G), and wireless LAN (IEEE 802.11a/b/g) to provide wireless service with higher data
rate and mobility. More advanced wireless systems targeted for even higher spectral efficiency
such as Ultrawideband (UWB) systems, and Wi-MAX (IEEE 802.16), as well as Wi-MAN (IEEE
802.20) [1] systems are being actively investigated in both academic and industrial communities.
According to a recent report in the Digital Media News for Europe
1
, the number of worldwide
mobile phone subscribers is expected to grow from 2 billion in 2005 to approximately 3.3 billion in

2010. The rapid worldwide growth in mobile phone subscribers has demonstrated conclusively that
wireless communication has become a robust and viable voice and data transport mechanism. The
demand for wireless services from the regular low rate voice telephony services to mixed voice/data
and multimedia services with higher data rate and quality of service (QoS) has conversely fuelled
the wireless technique advancement and revolution.
Nevertheless, realizing reliable and efficient communication over the wireless channel has been
a very challenging topic since the 1950s and 1960s. This is attributed to the hostile nature of the
1
/>2
wireless channel characterized by small-scale fading and large-scale fading [2]. For instance, the
transmission of signals over the wireless channels is affected by time-varying channel attenuation,
called fading. The received signal strength can fluctuate over a wide range of 80dB in the order of
milliseconds. In general, the fading effects of wireless channels impose additional challenges for
signal transmissions aside from the regular channel noise.
In order to satisfy the high data rate requirement and efficiently support multimedia services
in future wireless systems, many key enabling technologies have been burgeoning in a stable way.
Some of them are listed in [3] which include:
• Modulation and multiple access schemes with high spectral efficiency and flexibility such
as orthogonal frequency division multiplex (OFDM), multi-carrier code division multiple
access (MC-CDMA) and orthogonal frequency division multiple access (OFDMA) [4]
• Multiple antenna technology providing high spectral efficiency referred to as Multiple-Input
Multiple-Output (MIMO) technology
• A scalable network based on IP which seamlessly integrates heterogenous wireless applica-
tions [5]
• Intelligent resource management through cross-layer designs [6, 7]
Among the key enabling technologies, the multiple-antenna technique and the cross-layer de-
signs are the two recent and very promising approaches which deal with the harsh wireless trans-
missions [7]. Throughout this thesis, we shall exploit the significant performance advantages
brought about by a combination of these two techniques.
1.2 Literature Survey

It was pointed out by Shannon [8] in 1948 that the maximal achievable rate for an error free
transmission of a communication link is limited by the available bandwidth and power. The tradi-
tional way of increasing the data rate is to increase the bandwidth or power for transmission. Yet
either increasing power or broadening bandwidth becomes inadvisable when the required data rate
3
becomes high and the spectrum is limited. Recently, another interesting approach to increasing the
bit rate is to make use of multiple transmit and receive antennas. It has been shown by the pioneer-
ing works [9,10] that the channel capacity of the MIMO system is proportional to n = min[n
T
, n
R
]
where n
T
is the number of transmit antenna and n
R
is the number of receive antenna. With the mul-
tiple antennas, the wireless channel is transformed into a Multiple-Input Multiple-Output (MIMO)
channel where the link capacity gain is contributed by the n spatial channels created. Thus it
would be interesting to study the performance of future wireless systems equipped with multiple
antennas.
For the point-to-point MIMO link, the performance measure is relatively simple. For example,
we would like to improve link reliability by exploiting the spatial diversity [11–13] or apply spatial
multiplexing schemes to increase spectral efficiency [14–16]. However, for multi-user systems,
optimizing the individual link performance is not always the best approach when higher-layer
objectives such as system throughput, fairness and QoS requirement are considered. It is thus very
important to consider the upper-layer resource allocation together with the adaptive physical layer
design in a multi-user MIMO wireless system, which is demonstrated to be a method that can
completely exploit the temporal dimension (scheduling), the spatial dimension (multiple antennas)
and the multi-user dimension (multi-user diversity) in the resource space to achieve a good system-

level performance [17–19].
Cross-layer scheduling design has attracted intensive attention recently [20–23]. In [24, 25], a
joint design of the MAC layer and link layer has been shown to achieve significant gains over the
isolated design approach within each layer for single antenna systems. This gain is contributed by
the multi-user diversity, which is achieved by scheduling transmissions to users when their instan-
taneous channel quality is near the peak. Cross-layer scheduling design in multi-antenna system
has been investigated in [26–28] and the total system throughput is maximized by exploiting the
spatial diversity provided by multiple antennas and the inherent multi-user diversity simultane-
ously.
As perceived from the existing works mentioned above, channel state information (CSI) is
a fundamental requirement for the cross-layer scheduling design. With CSI knowledge, channel
4
adaptive techniques can be applied in both the physical layer and the MAC layer to improve system
performance. CSI is either obtained by channel estimation in TDD systems or CSI feedback to the
transmitter in FDD systems. In most of the works mentioned above, perfect CSI knowledge or the
perfect CSI feedback is assumed. Thus, in the above cases, it is enough to consider the ergodic
capacity as the performance objective since there would be no packet outage with perfect CSI as
long as the error correction code is sufficiently strong.
There are also some recent works on MIMO link capacity with imperfect CSI [29–31] and
cross-layer design with limited channel feedback. The effect of imperfect CSIT on point-to-point
multiple input multiple output (MIMO) link capacity has been investigated by [30, 32, 33]. It has
been found that mutual information degrades significantly due to channel estimation error. These
works are mainly on point-to-point MIMO case.
In wireless systems with multimedia applications, scheduling is an efficient way to ensure
quality of service (QoS) requirements in terms packet delay, throughput requirement and packet
error probability requirement [34, 35]. In [36] a cross-layer design for multi-user scheduling at
the data link layer is developed to provide guaranteed quality-of-service (QoS) for multimedia
applications over wireless fading channels, in which the QoS is interpreted in terms of outage of
probability of each user. In [37], the users’ transmission rate and power are adapted based upon
channel state information as well as the buffer occupancy; the objective is to regulate both the long-

term average transmission power and the average buffer delay incurred by the traffic. Connections
to the delay-limited capacity and the expected capacity of fading channels are discussed. Yet,
most of these works are designed for single antenna multi-user systems. In a recent work on QoS-
based scheduling design in multi-user MIMO system [38], the base station antennas are allocated
to users based on certain priority functions at each time slot. The priority functions capture the
user QoS demands quantified in terms of throughput and delay. Instead of providing a systematic
framework to satisfy the QoS requirements, the scheduling in this work is designed to achieve a
tradeoff between throughput, delay and fairness with a simple priority-based scheduling scheme.
5
1.3 Motivation and Problem Statement
It is important to apply the cross-layer design framework in wireless systems. However, most
of the existing works assume perfect CSI estimation or perfect feedback and the data rate of the
physical layer of the base station is commonly assumed to be continuously adaptive to the CSIT.
These assumptions are not practical in most wireless systems. Moreover, the conventional measure
of the total system ergodic capacity may not be too meaningful in the presence of imperfect CSIT
since there may be capacity outage during the transmission. To take into consideration of the
potential packet outage, we define the average system goodput as the optimization objective, which
measures the average total b/s/Hz successfully delivered to the receivers.
In short, there are still lots of open issues in the cross-layer scheduling
2
in multi-user multi-
antenna systems. Some of them are listed below:
• In practice, the CSIT could never be perfect and this shall greatly affect the scheduler’s
performance. What is the effect of CSIT imperfectness on the system capacity with the
naive space-time scheduler (designed for perfect CSIT), and how to obtain robust scheduling
performance by taking the CSIT error statistics into the cross-layer design pose a problem.
• Practical physical layer can only support a discrete set of rates due to the finite choice of
error correction encoders and discrete level constellations. Given that we have to support
Q transmission modes and have only imperfect CSIT available, what throughput (specific
channel encoder rate and modulation constellation) to choose and how to determine the right

transmission mode at the base station becomes a challenging problem.
• Most of the existing cross-layer scheduling designs assumed homogeneous user type (pure
delay-tolerant data application), which is not a realistic assumption for modelling next gener-
ation wireless multimedia traffic having heterogeneous classes. How to design a cross-layer
scheduler to exploit the spatial multiplexing gain as well as the multi-user selection diversity
2
The cross-layer scheduler in this thesis refers to the multi-user scheduling that exploits the spatial multiplexing
gains and the multi-user selection diversity gain through the knowledge of CSIT at the base station.
6
gain, and at the same time maintain the delay requirements of the delay sensitive users is still
an open problem.
In this thesis, we will focus on the three issues mentioned above and propose schedulers with
robust performance and QoS guarantees (in terms of delay constraints).
1.4 Thesis Research and Contributions
This thesis presents a study on cross-layer (joint MAC-PHY layer) space time scheduling de-
sign in multi-user MIMO wireless systems with a multi-antenna base station and K single-antenna
mobiles. We impose different physical layer and network layer constraints which include average
power, linear transmit processing (Zero-forcing or MMSE beamforming), imperfect CSIT, finite
rate set, and heterogenous delay constraints.
Our main contribution is to propose a systematic framework to address the cross-layer schedul-
ing problem in the presence of outdated CSI or imperfect CSI. The objective is to maximize the
total system goodput (instead of throughput when outage is considered) under average total power
constraint and linear transmit constraint. When heterogeneous applications (with different delay
constraints requirement) are considered, the design utility maximizes the total system throughput
subject to the delay constraint such that all the delay requirements are satisfied.
After presenting the background knowledge in Chapter 2, we shall first investigate the uplink
cross-layer design for multi-antenna systems with outdated channel state information in Chapter
3. We consider a multi-user single-input multiple-output (SIMO) system with one base station
(with N
R

receive antennas) and K mobile users (each with single transmit antenna). The multi-
user physical layer is modeled based on information theoretical framework and the cross-layer
design can be cast as an optimization problem. We found that with outdated CSI, there is signif-
icant degradation in the spatial multiplexing and multi-user diversity gain due to potential packet
transmission outage as well as mis-scheduling. Two heuristic but effective schemes, namely the
rate quantization and rate discounting, are proposed to obtain robust scheduling performance. It
is well-known that rate quantization imposes system capacity loss in systems with perfect CSI.
7
However, we found that the rate quantization in scheduling can enhance the system goodput and
robustness in the presence of outdated CSI.
In Chapter 4, the downlink scheduling and rate adaptation are investigated in multi-user multiple-
input single-output (MISO) systems with imperfect CSIT and finite rate set constraints. We pro-
pose a systematic analytical design framework to address the design issues regarding rate adapta-
tion and robust multi-user scheduling based on information theoretical approach. Due to the imper-
fect CSIT, there is potential packet outage. We formulate the cross-layer design as a mixed convex
and combinatorial optimization problem to maximize the average system goodput with respect to
the imperfect CSIT statistics and the discrete rate set constraint. By applying a tight Chi-square
approximation on the outage probability, we obtain a closed-form solution for the rate and power
adaptation for any given target packet error probability (PER). Numerical results demonstrate that,
by considering the statistic of CSIT errors into the design, the proposed scheduling scheme pro-
vides significant performance enhancement.
In Chapter 5 and 6, we shall take a different view by focusing on the design and performance
analysis of cross-layer schedulers targeted for heterogenous user types with mixed voice and data
applications. Specifically, we consider a wireless multimedia system with a base station (equipped
with n
T
transmit antennas) and two classes of single-antenna client users (running delay sensitive
voice application and delay insensitive data application). In Chapter 5, we shall propose a low-
complexity scheduler for the heterogeneous user applications, in which priority is heuristically
given to the delay sensitive voice users. Multi-user selection diversity and spatial multiplexing are

thereafter exploited among the remaining resource. The performance of the proposed scheduler
and the interaction between the data users (which are bursty) and the VoIP users (which are delay
sensitive) are thoroughly studied. It is demonstrated that the proposed heterogeneous scheduler
ensures a stable quality of service for VoIP users in the presence of bursty
data
traffic. In Chapter
6, an analytical cross-layer design framework is proposed for the multi-user multi-antenna sys-
tems with heterogeneous delay requirements. The proposed scheme optimally exploits the spatial
multiplexing gain and the multi-user selection diversity gain in terms of maximizing the system
capacity with delay constraints satisfied.
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Chapter 2
Background
2.1 Wireless Fading Channel - Characterizations and Mitigation
In wireless transmission, a signal can travel from the transmitter to the receiver over multi-
ple reflective paths. This phenomenon is referred to as multi-path propagation. Due to multiple
propagation paths, the received signals consist of multiple delayed and attenuated copies of the
transmitted signal. This phenomena actually places fundamental limitations on the performance of
wireless communication systems [2, 39, 40]. The degradation categories due to wireless transmis-
sion and the typical mitigation techniques to counteract the degradation shall be reviewed in this
section.
As illustrated in Fig. 2.1, there are typically two types of fading effect that characterize mobile
communications: large-scale propagation loss and small-scale multipath fading [39]. Large-scale
propagation loss is caused by path loss due to motion over large areas and shadowing due to
changes in terrain or obstacles, which usually fluctuates in a slow way. On the other hand, small-
scale multipath fading characterizes the variation of the received signal strength. It is caused by the
constructive or destructive superposition of the multiple paths depending on the time varying path
attenuation and delay, which usually varies rapidly and dramatically. The received signal through
wireless channel typically experiences both types of fading with small-scale fading superimposed
on large-scale propagation loss.

In wireless systems, the analysis on fading models plays an important role. The large-scale
fading (path loss and shadowing) model is used for system planning, power control, link budget and