Transmission and Reception with Multiple Antennas: Theoretical Foundations

167 Pages·2004·1.73 MB·English

by  Ezio Biglieri

Transmission and
Reception with
Multiple Antennas:
Theoretical
Foundations

Transmission and
Reception with Multiple
Antennas: Theoretical
Foundations
Ezio Biglieri
PolitecnicodiTorino
CorsoDucadegliAbruzzi24
TorinoI-10129,Italy
[email protected]
Giorgio Taricco
PolitecnicodiTorino
CorsoDucadegliAbruzzi24
TorinoI-10129,Italy
[email protected]
Boston – Delft
Foundations and TrendsR in
(cid:1)
Communications and Information Theory
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Printed on acid-free paper
ISBN: 1-933019-01-8; ISSNs: Paper version 1567-2190; Electronic
version 1567-2328
c 2004 E. Biglieri and G. Taricco
(cid:1)
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Contents
1 Introduction 1
2 Preliminaries 3
3 Channel models 9
3.1 Fading channel 9
3.2 Dynamics of the fading channel 12
3.3 MIMO channel 15
3.4 Narrowband multiple-input, multiple-output channel mod1e7ls
3.5 Channel state information 20
4 Channel capacity 21
4.1 Deterministic channel 22
4.2 Ergodic Rayleigh fading channel 25
4.3 Correlated channels 36
v
vi Contents
4.4 A critique to asymptotic analysis [29] 38
4.5 Nonergodic Rayleigh fading channel 40
5 Influence of channel-state information 53
5.1 Perfect CSI at the receiver 53
5.2 Imperfect CSI 54
5.3 Imperfect CSI at the receiver: General guidelines 57
5.4 CSI at the transmitter and at the receiver 66
6 Coding for multiple-antenna systems 71
6.1 Maximum likelihood detection 72
7 Some practical coding schemes 79
7.1 Delay diversity 79
7.2 Alamouti scheme 79
7.3 Alamouti scheme revisited: Orthogonal designs 82
7.4 Linear space–time codes 83
7.5 Trellis space–time codes 85
7.6 Space–time codes when CSI is not available 85
8 Suboptimum receiver interfaces 87
8.1 Linear interfaces 88
8.2 Nonlinear interfaces 92
9 The fundamental tradeoff 101
9.1 2 2 schemes 104
×
9.2 Orthogonal designs 105
9.3 Zero-forcing vertical BLAST 105
9.4 Zero-forcing diagonal BLAST 106
9.5 MIMO transmission in a multiuser environment 106
9.6 Derivation of the diversity–rate tradeoff curve 107
A Complex random variables and vectors 111
Contents vii
A.1 Gaussian random variables 112
A.2 Real random vectors 112
A.3 Complex random vectors 113
B Results from information theory 117
B.1 A basic inequality 117
B.2 Capacity of the Gaussian MIMO channel 118
B.3 Ergodic capacity 120
C Random matrices 123
D Numerical calculation of error probabilities 127
D.1 Application: MIMO channel 129
E Two proofs 133
E.1 Proof of (4.23) 133
E.2 Proof of (4.51) 137
Acknowledgements 145
References 147
Notations and Acronyms 157

1
Introduction
The fundamental characteristic of wireless communication, that is, its
ability to connect users in motion, represents an attractive feature for
users, but at the same time a strong challenge to systems designers.
Theappeal of wireless systems is documented by their commercial suc-
cess, with projections indicating that in the near future the number of
their subscribers will exceed that of wireline subscribers.Concurrently,
users expect a growing variety of sophisticated services along with in-
creasingly higher quality, which contrasts with the difficulties involved
with transmission on a mobile radio channel. On one hand,multimedia
services require high data rates, while mobility requires networking
with seamless connectivity. On the other hand, bandwidth and power
are limited, interferences from other users impair transmission quality,
mobility causes rapid variations of the physical channel, portability de-
mandsa long battery life, etc. All this makes it very difficultto provide
high-quality mobile services.
Up until recently, it was accepted that the degrees of freedom to
be allocated to system design could only obtained from time or band-
width. Of late, it was discovered that space, obtained by increasing the
number of transmit and receive antennas, can also effectively generate
1