Ill
`
`wArogyaswami Paulraj, Rohit NabarTand Dhananjay Gore
`
`
`
`|PR2018—01477
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`

`

`Introduction to Space-Time
`
`Wireless Communications
`
`Arogyaswami Paulraj
`
`Stanford University
`
`Rohit Nabar
`
`ETH, Zurich
`
`Dhananjay Gore
`Stanford University
`
`m» CAMBRIDGE
`
`a; :23 UNIVERSITY PRESS
`
`|PR2018—01477
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`

`

`PUBLISHED BY THE PRESS SYNDICATE OF THE UNIVERSITY OF CAMBRIDGE
`
`The Pitt Building. Trumpington Street. Cambridge. United Kingdom
`CAMBRIDGE UNIVERSITY PRESS
`
`The Edinburgh Building. Cambridge C32 2RU, UK
`40 West 20th Street, New York. NY 1001 142] 1. USA
`477 “filliamstown Road. Port Melbourne. VIC 3207. Australia
`Ruiz de Alarcon 13. 28014 Madrid. Spain
`Bank House, The Waterfront, Cape Town 800l. South Africa
`
`http://www.cambridgc.org
`
`© Cambridge University Press 2003
`
`This book is in copyright. Subject to statutory exception
`and to the provisions of relevant collective licensing agreements.
`no reproduction of any part may take place without
`the written permission of Cambridge University Press.
`
`First published 2003
`
`Printed in the United Kingdom at the University Press. Cambridge
`
`Typefaces Times 10.5i14 pt and Helvetica Neue
`
`System [HEX 25
`
`[TB]
`
`A catalog recardfar this book is available from the British Library
`
`ISBN 0 521 82615 2 hardback
`
`
`
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`page xiv
`
`xxii
`
`xxiii
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`xxvi
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`xxix
`
`1
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`1
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`6
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`7
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`7
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`8
`8
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`9
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`11
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`1 1
`
`11
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`12
`
`12
`18
`20
`
`22
`22
`23
`24
`
`Contents
`
`List offigm'cs
`
`L1.” qubles‘
`
`Preface
`
`Li's! (gfabbreviai‘ions
`
`Li's! rgfsvmbols
`
`Introduction
`
`— 1
`
`1.1 History of radio, antennas and array signal processing
`
`1.2 Exploiting multiple antennas in wireless
`
`1.2.1 Array gain
`
`1.2.2 Diversity gain
`
`1.2.3 Spatial multiplexing (SM)
`1.2.4 Interference reduction
`
`1.3 ST wireless communication systems
`
`ST propagation
`
`— 2
`
`2.1 Introduction
`
`2.2 The wireless channel
`
`2.2.1 Path loss
`
`2.2.2 Fading
`2.3 Scattering model in macrocells
`2.4 Channel as a ST random field
`
`2.4.1 Wide sense stationarity (W88)
`2.4.2 Uncorrelated scattering (US)
`2.4.3 Homogeneous channels (H0)
`2.5 Scattering functions
`
`vii
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`

`Contents
`viii
` —
`
`27
`
`28
`
`29
`
`3]
`
`32
`
`32
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`33
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`32
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`33
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`33
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`34
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`34
`37
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`37
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`38
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`40
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`40
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`41
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`41
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`43
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`43
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`43
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`44
`45
`
`48
`48
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`49
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`51
`
`52
`
`53
`54
`
`54
`
`55
`
`56
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`56
`58
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`2.6 Polarization and field diverse channels
`
`2.7 Antenna array topology
`
`2.8 Degenerate channels
`
`2.9 Reciprocity and its implications
`
`ST channel and signal models
`
`— 3
`
`3.1 Introduction
`
`3.2 Definitions
`
`3.2.1 SISO channel
`
`3.2.2 SIMO channel
`
`3.2.3 MISO channel
`
`3.2.4 MIMO channel
`
`3.3 Physical scattering model for ST channels
`3.3.1 SIMO channel
`
`3.3.2 MISO channel
`
`3.3.3 MIMO channel
`
`3.4 Extended channel models
`
`3.4.1 Spatial fading correlation
`
`3.4.2 LOS component
`
`3.4.3 Cross-polarized antennas
`
`3.4.4 Degenerate channels
`
`3.5 Statistical properties of H
`
`3.5.1 Singular values of H
`
`3.5.2 Squared Frobenius norm of H
`3.6 Channel measurements and test channels
`
`3.7 Sampled signal model
`3.7.1 Normalization
`
`3.7.2 3180 sampled signal model
`
`3.7.3 SIMO sampled signal model
`
`3.7.4 MISO sampled signal model
`
`3.7.5 MIMO sampled signal model
`3.8 ST multiuser and ST interference channels
`
`3.8.1 ST multiuser channel
`
`3.8.2 ST interference channel
`
`3.9 ST channel estimation
`
`3.9.1 Estimating the ST channel at the receiver
`3.9.2 Estimating the ST channel at the transmitter
`
`
`
`
`
`.._.tr..,__..‘._____.___r.“
`
`
`
`IIJEl-gll131'
`
`ll
`cl.“'1
`u'
`I!“
`
`
`
`.1..114.*1.\*.P_'.1‘«.-..._.;.,~_;
`
`
`
`31141.55?“1'”.-
`
`I”![III
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`

`

`63
`
`63
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`63
`65
`
`66
`
`70
`
`7'1
`
`71
`72
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`77
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`77
`
`78
`
`80
`
`80
`
`81
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`86
`
`86
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`86
`89
`90
`90
`92
`93
`
`95
`
`97
`
`98
`100
`102
`102
`104
`105
`106
`
`
`
`ix Contents—
`
`Capacity of ST channels
`
`4.1 Introduction
`
`_ 4
`
`4.2 Capacity of the frequency [lat deterministic MIMO channel
`4.3 Channel unknown to the transmitter
`
`4.4 Channel known to the transmitter
`
`4.4.1 Capacities ofSlMO and MISO channels
`
`4.5 Capacity of random MIMO channels
`
`~1.5.1 Capacity 01wa channels for large M
`4.5.2 Statistical characterization of the information rate
`
`4.6 Influence of Ricean fading. fading correlation. XPD and degeneracy on
`
`MIMO capacity
`
`4.6.1 Influence of the spatial fading correlation
`
`4.6.2 Influence of the LOS component
`
`4.6.3 Influence of XPD in a non—fading channel
`
`4.6.4 Influence of degeneracy
`
`4.7 Capacity of frequency selective MIMO channels
`
`Spatial diversity
`
`5.1 Introduction
`
`— 5
`
`5.2 Diversity gain
`5.2.1 Coding gain vs diversity gain
`5.2.2 Spatial diversity vs time/frequency diversity
`5.3 Receive antenna diversity
`5.4 Transmit antenna diversity
`5.4.] Channel unknown to the transmitter: MISO
`
`5.4.2 Channel known to the transmitter: MISO
`
`5.4.3 Channel unknown to the transmitter: MIMO
`
`5.4.4 Channel known to the transmitter: MIMO
`5.5 Diversity order and channel variability
`5.6 Diversity performance in extended channels
`5.6.1 Influence of signal correlation and gain imbalance
`5.6.2 Influence of Ricean fading
`5.6.3 Degenerate MIMO channels
`5.7 Combined space and path diversity
`
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`

`
`
`Contents— )1
`
`108
`
`108
`
`108
`
`109
`
`1 [J
`
`1
`
`112
`
`1 13
`
`1 14
`
`11Jr
`
`1 15
`
`1 17
`123
`
`123
`
`126
`
`129
`
`129
`
`I31
`
`137
`
`137
`
`137
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`137
`
`138
`143
`
`143
`
`144
`148
`
`148
`149
`
`158
`159
`159
`
`5.8 Indirect transmit diversity
`
`5.8.1 Delay diversity
`
`5.8.2 Phase—roll diversity
`
`5.9 Diversity ofa space—time—frequency selective fading channel
`
`_ 6
`
`ST coding without channel knowledge at transmitter
`
`6.1 Introduction
`
`6.2 Coding and interleaving architecture
`
`6.3 ST coding for frequency fiat channels
`
`6.3.1 Signal model
`
`6.3.2 ST codeword design criteria
`
`6.3.3 ST diversity coding (r5 5 1)
`6.3.4 Performance issues
`
`6.3.5 Spatial multiplexing as a ST code (rI : MT)
`
`6.3.6 ST coding for intermediate rates (1 < rs < MT)
`
`6.4 ST coding for frequency selective channels
`
`6.4.1 Signal model
`
`6.4.2 ST codeword design criteria
`
`— 7
`
`
`
`ST receivers
`
`7.1 Introduction
`
`7.2 Receivers: 8180
`
`7.2.] Frequency flat channel
`
`7.2.2 Frequency selective channel
`7.3 Receivers: SIMO
`
`7.3.1 Frequency flat channel
`
`7.3.2 Frequency selective channels
`7.4 Receivers: MIMO
`
`7.4.1 ST diversity schemes
`7.4.2 SM schemes
`
`7.4.3 SM with horizontal and diagonal encoding
`7.4.4 Frequency selective channel
`7.5 Iterative MIMO receivers
`
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`

`

`xi
`
`— _ 8
`
`Exploiting channel knowledge at the transmitter
`
`8.1 Introduction
`
`8.2 Linear pre-filtering
`
`8.3 Optimal pre-filtering for maximum rate
`
`8.3.1 Full channel knowledge
`
`8.3.2 Partial channel knowledge
`
`8.4 Optimal pre-filtering for error rate minimization
`
`8.4.1 Full channel knowledge
`
`8.4.2 Partial channel knowledge
`8.3 Selection at the transmitter
`
`8.5.1 Selection between SM and diversity coding
`8.5.2 Antenna selection
`
`8.6 Exploiting imperfect channel knowledge
`
`ST OFDM and spread spectrum modulation
`
`9.1 Introduction
`
`9.2 SISO—OFDM modulation
`
`9.3 MIMO-OFDM modulation
`
`9.4 Signaling and receivers for MIMO-OFDM
`9.4.1 Spatial diversity coding for MIMO-OFDM
`9.4.2 SM for MIMO»OFDM
`
`9.4.3 Space-frequency coded MIMO—OFDM
`9.5 8180—35 modulation
`
`9.5.1 Frequency fiat channel
`9.5.2 Frequency selective channel
`9.6 MIMO—SS modulation
`
`9.7 Signaling and receivers for MIMO-SS
`9.7.1 Spatial diversity coding for MIMO-SS
`9.7.2 SM for MIMO-SS
`
`— 9
`
`Contents
`
`
`163
`
`163
`
`163
`
`165
`
`165
`
`166
`
`168
`
`168
`
`168
`171
`
`171
`172
`
`175
`
`178
`
`178
`
`178
`
`182
`
`184
`184
`186
`
`186
`188
`
`188
`191
`193
`
`194
`194
`197
`
`199
`
`199
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`MlMO-multiuser
`
`10.1
`
`Introduction
`
`— 1
`
`0
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`201
`
`201
`
`202
`
`207
`208
`
`208
`
`208
`
`209
`
`2 l3
`
`214
`
`215
`216
`
`216
`
`2 l 8
`
`218
`
`219
`
`219
`
`220
`
`222
`
`223
`
`224
`
`224
`
`226
`
`228
`
`229
`
`230
`
`230
`
`231
`
`232
`
`233
`233
`
`234
`
`235
`237
`
`237
`
`
`
`xii Contents—
`
`10.2 MIMO—MAC
`
`10.2.1
`
`Signal model
`
`10.2.2 Capacity region
`
`10.2.3 Signaling and receiver design
`10.3 MIMO-BC
`
`10.3.1
`
`Signal model
`
`10.3.2 Forward link capacity
`
`10.3.3 Signaling and receiver design
`
`10.4 Outage performance of MIMO-MU
`
`10.4.1 MU vs SU — single cell
`
`10.4.2 MU single cell vs SU multicell
`10.5 MIMO~MU with OFDM
`
`10.6 CDMA and multiple antennas
`
`ST cc-channel interference mitigation
`
`_ 1
`
`1
`
`11.1
`
`Introduction
`
`11.2 CCI characteristics
`
`11.3 Signal models
`
`11.3.1
`
`SIMO interference model (reverse link)
`
`11.3.2 MIMO interference channel (any link)
`
`11.3.3 MISO interference channel (forward link)
`
`11.4 CCI mitigation on receive for SIMO
`
`11.4.1
`
`Frequency fiat channel
`
`11.4.2 Frequency selective channel
`
`11.5 CCI mitigating receivers for MIMO
`
`11.5.1 Alamouti coded signal and interference (MT = 2)
`
`11.6 CCI mitigation on transmit for M80
`
`11.6.1 Transmit-MRC or matched beamforming
`
`11.6.2 Transmit ZF or nulling beamfonner
`
`11.6.3 Max SINR beamfonning with coordination
`
`Joint encoding and decoding
`11.7
`11.8 SS modulation
`
`11.8.1
`
`ST—RAKE
`
`11.8.2 ST pre—RAKE
`11.9 OFDM modulation
`
`11.10 Interference diversity and multiple antennas
`
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`

`240
`
`240
`
`240
`
`241
`
`244
`244
`
`245
`
`246
`
`249
`250
`251
`
`254
`271
`272
`
`
`
`xiii Contents—
`
`Performance limits and tradeofis in MIMO channels
`
`— 1
`
`2
`
`12.1
`
`introduction
`
`12.2 Error performance in fading channels
`
`l2.3
`
`12.4
`
`Signaling rale vs PER vs SNR
`
`Spectral efficiency of ST coding/receiver techniques
`12.4.1 D—BLAST
`
`12.4.2 OSTBC
`
`12.4.3
`
`ST receivers for SM
`
`12.4.4 Receiver comparison: Varying Mgr/MR
`System design
`12.5
`12.6 Comments on capacity
`
`References
`Index of common variables
`Subjec! index
`
`|PR2018—01477
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`

`

`
`
`xxx List of symbols—
`
`:‘(A)
`7?,
`
`rank of the matn‘x A
`real field
`
`EMA}. %{A}
`
`real and imaginary parts of A, respectively
`
`trace of A
`
`‘
`
`t
`
`f
`
`unlts ep unc 101'],
`
`t‘
`
`d 6
`
`d
`
`e ne asap.)
`
`.- =
`
`lif.\'>0..\‘ER
`—
`
`[0 if x < 0.x 6 7?.
`
`Tr(A)
`
`x
`
`u(\)
`
`vec(A)
`
`x
`
`()+
`
`Z
`
`stacks A into a vector columnwise'
`x ifx >0..\' ER
`_
`
`defined as .' =
`
`m"
`
`[0 if.t<0,.reR
`
`integer field
`
`'IfA = [al a;
`
`an] ism x n. then vec(A) = [allr a;
`
`3;]T is rm: x I.
`
`
`
`|PR2018—01477
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`

`

`1
`
`Introduction
`
`The radio age began over a 100 years ago with the invention of the radiotelegraph by
`Guglielmo Marconi and the wireless industry is now set for rapid growth as we enter a
`
`new century and a new millennium. The rapid progress in radio technology is creating
`
`new and improved services at lower costs, which results in increases in air—time usage
`
`and the number of subscribers. Wireless revenues are currently growing between 20%
`
`and 30% per year, and these broad trends are likely to continue for several years.
`
`Multiple access wireless communications is being deployed for both fixed and mobile
`
`applications. In fixed applications, the wireless networks provide voice or data for fixed
`
`subscribers. Mobile networks offering voice and data services can be divided into two
`
`classes: high mobility, to serve high speed vehicle-home users, and low mobility, to
`
`serve pedestrian users. Wireless system designers are faced with a number of challenges.
`
`These include the limited availability of the radio frequency spectrum and a complex
`
`time—varying wireless environment (fading and multipath). In addition, meeting the
`
`increasing demand for higher data rates, better quality of service (Q08), fewer dropped
`
`calls, higher network capacity and user coverage calls for innovative techniques that
`
`improve spectral efficiency and link reliability. The use of multiple antennas at the
`
`receiver and/or transmitter in a wireless system, popularly known as space~time (ST)
`
`wireless or multiantenna communications or smart antennas is an emerging technology
`
`that promises significant improvements in these measures. This book is an introduction
`
`to the theory of ST wireless communications.
`
`1.1
`
`History of radio, antennas and array signal processing
`
`The origins of radio date back to 1861 when Maxwell, while at King‘s College in
`London, proposed a mathematical theory of electromagnetic (EM) waves. A practical
`demonstration of the existence of such waves was performed by Hertz in 1887 at the
`
`University of Karlsruhe, using stationary (standing) waves. Following this, improve—
`
`ments in the generation and reception of EM waves were pursued by many researchers
`
`in Europe. In 1890, Branly in Paris developed a “coherer” that could detect the presence
`of EM waves using iron filings in a glass bottle. The coherer was further refined by
`
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`

`

`Introduction
`1
`
`
`Right at the University of Bologna and Lodge in England. Other contributions came
`from Popov in Russia. who is credited with devising the first radio antenna during his
`
`attempts to detect EM radiation from lightning.
`In the summer of 1895. Marconi. at the age of21. was inspired by the lectures on radio
`
`waves by Righi at the University ofBologna and he built and demonstrated the first radio
`
`telegraph. He used Hertz‘s spark transmitter. Lodge’s coherer and added antennas to as
`
`semble his instrument. In 1898. Marconi improved the telegraph by adding a four—circuit
`
`tuning device. allowing simultaneous use of two radio circuits. That year. his signal
`
`bridged the English Channel. 52 km wide. between Wi mereux and Dmer. His other tech-
`
`nical developments around this time included the magnetic detector. which was an im-
`
`provement over the less efficient coherer, the rotatory spark and the use of directive an-
`
`tennas to increase the signal level and to reduce interference in duplex receiver circuits.
`
`In the next few years. Marconi integrated many new technologies into his increasingly
`
`sophisticated radio equipment, including the diode valve developed by Fleming. the
`
`crystal detector, continuous wave (CW) transmission developed by Poulsen. Fessenden
`
`and Alexanderson. and the triode valve or audio developed by Forrest.
`
`Civilian use of wireless technology began with the installation of the first 2 MHz
`
`land mobile radiotelephone system in 1921 by the Detroit Police Department for police
`
`car dispatch. The advantages of mobile communications were quickly realized. but its
`
`wider use was limited by the lack of channels in the low frequency band. Gradually.
`
`higher frequency bands were used, opening up the use of more channels. A key ad—
`
`vance was made in 1933, when Armstrong invented frequency modulation (FM). which
`
`made possible high quality radio communications. In 1946. a Personal Correspondence
`
`System introduced by Bell Systems began service and operated at 150 MHZ with speech
`
`channels 120 kHz apart. As demand for public wireless services began to grow, the
`
`Improved Mobile Telephone Service (IMTS) using FM technology was developed by
`
`AT&T. These were the first mobile systems to connect with the public telephone net-
`work using a fixed number of radio channels in a single geographic area. Extending
`such technology to a large number of users with full duplex channels needed excessive
`
`bandwidth. A solution was found in the cellular concept (known as cellularization)
`
`conceived by Ring at Bell Laboratories in 1947. This concept required dividing the
`service area into smaller cells, and using a subset of the total available radio channels
`
`in each cell. AT&T proposed the first high capacity analog cellular telephone system
`
`called the Advanced Mobile Phone Service (AMPS) in 1970. Mobile cellular systems
`
`have evolved rapidly since then, incorporating digital communication technology and
`
`serve nearly one billion subscribers worldwide today. While the Global System for
`
`Mobile (GSM) standard developed in Europe has gathered the largest market share,
`
`cellular networks in the USA have used the IS- 136 (using time division multiple access
`
`or TDMA) and IS»95 (using Code Division Multiple Access or CDMA) standards. With
`
`increasing use of wireless internet in the late 19905, the demand for higher spectral effi—
`ciency and data rates has led to the development of the so called Third Generation (3G)
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`
`Il'IIIlllllIIJJlIlIlII_—————i
`
`
`
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`

`

`1.1 History of radio, antennas and array signal processing
`
`TablelJ. Performance goalsfor (”Marinas in wireless
`communications
`
`
`
`
`
` Antenna design AOA estimation Link performance
`
`Gain
`Bandwidth
`Radiation pattern
`Size
`
`Error variance
`Bias
`Resolution
`
`Coverage
`Quality
`Interference reduction
`Spectral efficiency
`
`Active integrated
`
`Phased arrays
`
`[9605
`
`19805
`
`Yagi—Uda
`
`l900s
`Directive
`
`
`1880—18905
`HertflMarconi/Popov
`
`Patch
`
`[9505
`
`19205
`
`Figure 1.1: Developments in antenna (EM) technology.
`
`wireless technologies. 3G standardization failed to achieve a single common world-
`wide standard and now offers UMTS (wideband CDMA) and lXRTI‘ as the primary
`
`standards. Limitations in the radio frequency (RF) spectrum necessitate the use of
`
`innovative techniques to meet the increased demand in data rate and QoS.
`The use of multiple antennas at the transmitter and/or receiver in a wireless commu-
`nication link opens a new dimension H space, which if leveraged correctly can improve
`performance substantially. Table 1.1 details the three main areas of study in the field of
`radio antennas and their applications. The first covers the electromagnetic design of the
`antennas and antenna arrays. The goals here are to meet design requirements for gain,
`polarization, beamwidth, sidelobe level, efficiency and radiation pattern. The second
`area is the angle—of—arrival (AOA) estimation and, as the name indicates, focuses on
`estimating arrival angles of wavefronts impinging on the antenna array with minimum
`error and high resolution. The third area of technology that this book focuses on is the
`use of antenna arrays to improve spectral efficiency, coverage and quality of wireless
`links.
`
`A timeline of the key developments in the field of antenna design is given in Fig. 1.1.
`The original antenna design work came from Marconi and Popov among others in the
`early 19005. Marconi soon deveIOped directional antennas for his cross-Atlantic links.
`Antenna design improved in frequency of Operation and bandwidth in the early part of
`the twentieth century. An important breakthrough was the Yagi—Uda arrays that offered
`high bandwidth and gain. Another important development was the patch antenna that
`offers low profile and cost. The use of antennas in arrays began in World War II, mainly
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`

`4
`
`Introduction
`1
`
`
`Multiple source
`
`
`
`
`
`Maximum likelihood
`
`ESPRIT
`
`
`1985
`
`
`1980
`
`I964
`
`
`
`
`
`Single source
`
`Wul lcnweber
`
`1935
`
`Sweeny
`
`[970
`
`Adcock
`
`Loop
`
`Figure 1.2: Developments in AOA estimation.
`
`for radar applications. Array design brought many new issues to the fore. such as gain.
`
`beamwidth, sidelobe level, and beamsteering.
`
`The area of AOA estimation had its beginnings in World War I when loop antennas
`
`were used to estimate signal direction (see Fig. 1.2 for a timeline of ADA technol-
`
`ogy). Adeock antennas were a significant advance and were used in World War II.
`
`Wullenweber arrays were developed in 1938 for lower frequencies and where accuracy
`
`was important, and are used in aircraft localization to this day. These techniques ad-
`
`dressed the single source signal wavefront case. Ifthere are multiple sources in the same
`
`frequency channel or multipath arrivals from a single source. new techniques are needed.
`
`The problem of ADA estimation in the multisource case was properly addressed in the
`
`19703 and 19803. Capon’s method [Capon er a1.. 1967], a well—known technique. offered
`
`reasonable resolution performance although it suffered from bias even in asymptoti-
`
`cally large data cases. The multiple signal classification (MUSIC) technique proposed
`
`by Schmidt in 1981 was a major breakthrough. MUSIC is asymptotically unbiased and
`offers improved resolution performance. Later a method called estimation of signal
`
`parameters via rotational invariance techniques (ESPRIT) that has the remarkable ad-
`
`vantage of not needing exact characterization of the array manifold and yet achieves
`optimal perfortnance was proposed [Paulraj et at, 1986; Roy et aL, 1986].
`
`The third area of antenna applications in wireless communications is link enhance-
`
`ment (see Fig. 1.3). The use of multiple receive antennas for diversity goes back to
`
`Marconi and the early radio pioneers. So does the realization that steerable receive
`
`antenna arrays can be used to mitigate co-channel interference in radio systems. The
`
`use of antenna arrays was an active reseach area during and after World War II in radar
`
`systems. More sophisticated applications of adaptive signal processing at the wireless
`
`receiver for improving diversity and interference reduction had to wait until the 19705
`
`for the arrival of digital signal processors at which point these techniques were vigor-
`ously developed for military applications. The early 19905 saw new proposals for using
`antennas to increase capacity of wireless links. Roy and Ottersten in 1996 proposed the
`
`use of base-station antennas to support multiple co-channel users. Paulraj and Kailath in
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`

`5
`
`'
`'
`'
`1.1 Hi to
`ry 0! radio, antennas and array Signal processmg
`s
`— ___*________
`
`Tx—Rx ST techniques
`
`2000
`
` Adaptive
`Rx ST techniques
`
`19005
`
`
`
`
`
`
`
`
`
`Jammcr cancellation
`l9805
`
`
`Nnnnttlaptn‘c
`
`
`
`Marconi
`
`Butler
`
`[905
`
`Figure 1.3: Developments in antenna technology for link performance.
`
`Datarate
`
`(Mbps)
`
`SNR (dB)
`
`Figure 1.4: Data rate (at 95% reliability) vs SNR for different antenna configurations. Channel
`bandwidth is 200 KHz.
`
`1994 preposed a technique for increasing the capacity of a wireless link using multiple
`
`antennas at both the transmitter and the receiver. These ideas along with the fundamen-
`
`tal research done at Bell Labs [Telatar, 1995; Foschini, 1996; Foschini and Gans. 1998;
`
`Tarokh er mi, 1998] began a new revolution in information and communications theory
`
`in the mid 19903. The goal is to approach performance limits and to explore efficient but
`
`pragmatic coding and modulation schemes for wireless links using multiple antennas.
`Clearly much more work has yet to be done and the field is attracting considerable
`research talent.
`
`The leverage of ST wireless technology is significant. Figure 1.4 plots the maximum
`
`error—free data rate in a 200 KHz fading channel vs the signal to noise ratio (SNR)
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`

`V
`
`'2
`
`V x
`
`6
`
`Introduction
`
` 1
`
`a. .
`
`Figure 1.5: Antenna configurations in ST wireless systems (Tx: Transmitter. Rs: Receiver).
`
`that is guaranteed at 95% reliability. Assuming a target receive SNR of 20 dB. current
`
`single antenna transmit and receive technology can offer a data rate of 0.5 Mbps. A
`
`two-transmit and one—receive antenna system would achieve 0.8 Mbps. A four—transmit
`
`and four-receive antenna system can reach 3.75 Mbps. It is worth noting that 3.75 Mbps
`is also achievable in a single antenna transmit and receive technology. but needs 105
`
`times higher SNR or transmit power compared with a four—transmit and four-receive
`
`antenna configuration. The technology that can deliver such remarkable gains is the
`
`subject of this book.
`
`1.2
`
`Exploiting multiple antennas in wireless
`
`Figure 1.5 illustrates different antenna configurations for ST wireless links. 8180 (sin—
`gle input single output) is the familiar wireless configuration, SIMO (single input
`multiple output) has a single transmit antenna and multiple (MR) receive antennas.
`MISO (multiple input single output) has multiple (MT) transmit antennas and a sin-
`gle receive antenna and MIMO (multiple input multiple output) has multiple (Mr)
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`—
`7
`
`1.2 Exploiting multiple antennas in wireless
`—-_————__
`
`transmit antennas and multiple (MR) receive antennas. The MIMO-MU (MIMO mul-
`tiuscr) configuration refers to the case where a base—station with multiple (M) antennas
`communicates with P users each with one or more antennas. Both transmit and re-
`
`ceive configurations are shown. We sometimes abbreviate SIMO. MISC) and MlMO
`
`configurations as XIXO.
`
`1.2.1
`
`Array gain
`
`Array gain refers to the average increase in the SNR at the receiver that arises front the
`
`coherent combining effect of multiple antennas at the receiver or transmitter or both.
`
`Consider. as an example. a SIMO channel. Signals arriving at the receive antennas
`
`have different amplitudes and phases. The receiver can combine the signals coherently
`
`so that the resultant signal is enhanced. The average increase in signal power at the
`
`receiver is proportional to the number of receive antennas. In channels with multiple
`
`antennas at the transmitter (MISO or MIMO channels). array gain exploitation requires
`
`channel knowledge at the transmitter.
`
`1.2.2
`
`Diversity gain
`
`Signal power in a wireless channel fluctuates (or fades). When the signal power drops
`
`significantly, the channel is said to be in a fade. Diversity is used in wireless channels
`
`to combat fading.
`
`Receive antenna diversity can be used in SIMO channels flakes, 19741. The receive
`
`antennas sec independently faded versions of the same signal. The receiver combines
`
`these signals so that the resultant signal exhibits considerably reduced amplitude vari—
`
`ability (fading) in comparison with the signal at any one antenna. Diversity is characw
`
`terized by the number of independently fading branches, also known as the diversity
`
`order and is equal to the number of receive antennas in SIMO channels.
`
`Transmit diversity is applicable to MISO channels and has become an active area for
`
`' research [Wittnebem 1991; Seshadri and Winters. 1994; Kuo and Fitz. 1997; Olofsson
`
`er (11., 1997; Heath and Paulraj, 1999]. Extracting diversity in such channels is possible
`
`with or without channel knowledge at the transmitter. Suitable design of the transmitted
`
`signal is required to extract diversity. ST diversity coding [Seshadri and Winters, 1994;
`
`Guey e! at'., 1996; Alamouti, 1998; Tarokh er (1]., 1998, 1999b] is a transmit diversity
`technique that relies on coding across space (transmit antennas) to extract diversity
`in the absence of channel knowledge at the transmitter. If the channels of all transmit
`
`antennas to the receive antenna have independent fades, the diversity order of this
`
`channel is equal to the number of transmit antennas.
`Utilization of diversity in MIMO channels requires a combination of the receive and
`
`transmit diversity described above. The diversity order is equal to the product of the
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`

`

`8
`
`Introduction
`1
`
`
`number of transmit and receive antennas. if the channel between each transmitit'cceive
`
`antenna pair fades independently.
`
`1.2.3
`
`Spatial multiplexing (SM)
`
`SM offers a linear (in the number of transmit—receive antenna pairs or mint MR. MT ))
`
`increase in the transmission rate (or capacity) for the same bandwidth and uith no
`
`additional power expenditure. SM is only possible in MIMO channels [Pault'aj and
`
`Kailath, 1994; Foschirti, 1996; Telatar, 1999a]. In the following we discms the basic
`
`principles of SM for a system with two transmit and two receive antennas. The concept
`
`can be extended to more general MIMO channels.
`
`The bit stream to be transmitted is demultiplcxed into two half-rate substreams.
`
`modulated and transmitted simultaneously from each transmit antenna. Under favor-
`
`able channel conditions. the spatial signatures of these signals induced at the receive
`
`antennas are well separated. The receiver. having knowledge of the channel. can dif—
`
`ferentiate between the two co—channel signals and extract both signals. after which
`
`demodulation yields the original sub-streams that can now be combined to yield the
`
`original bit stream. Thus SM increases transmission rate proportionally with the number
`
`of transmit—receive antenna pairs.
`
`SM can also be applied in a multiuser format (MIMO~MU, also known as space
`
`division multiple access or SDMA). Consider two users transmitting their individual
`
`signals, which arrive at a base-station equipped with two antennas. The base—station
`
`can separate the two signals to support simultaneous use of the channel by both users.
`
`Likewise the base-station can transmit two signals with spatial filtering so that each
`
`user can decode its own signal adequately. This allows a capacity increase proportional
`to the number of antennas at the base—station and the number of users.
`
`1 2.4
`
`Interference reduction
`
`Co—channel interference arises due to frequency reuse in wireless channels. When mul—
`
`tiple antennas are used, the differentiation between the spatial signatures of the desired
`
`signal and co~channel signals can be exploited to reduce the interference. Interference
`
`reduction requires knowledge of the channel of the desired signal. However, exact
`knowledge of the interferer's channel may not be necessary.
`
`Interference reduction (or avoidance) can also be implemented at the transmitter,
`where the goal is to minimize the interference energy sent towards the co-channel users
`
`while delivering the signal to the desired user. Interference reduction allows the use of
`
`aggressive reuse factors and improves network capacity.
`
`We note that it may not be possible to exploit all the leverages simultaneously due
`to conflicting demands on the spatial degrees of freedom (or number of antennas). The
`degree to which these conflicts are resolved depends upon the signaling scheme and
`
`receiver design.
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`“3.1,'_l
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`ll‘llylllL-
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`It.
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`

`—
`9
`
`1.3 ST wireless communication systems
`
`
`
`interleavmg
`
`
`Modulation
`FiF
`preliltering
`
`
`'
`
`
`
` ST coding
`
`
`Deinterleaving
`'
`demodulation
`
`
`ST receiver
`postdlltenng
`
`
`
`Figure 1.6: Schematic ofa ST wireless communication system.
`
`1.3
`
`ST wireless communication systems
`
`Figure 1.6 shows a typical ST wireless system with MT transmit antennas and MR
`
`receive antennas. The input data bits enter a ST coding block that adds parity bits
`
`for protection against noise and also captures diversity from the space and possibly
`
`frequency or time dimensions in a fading environment. After coding. the bits (or words)
`
`are interleaved across space. time and frequency and mapped to data symbols (such
`
`as quadrature amplitude modulation (QAM)) to generate MT outputs. The MT symbol
`
`streams may then be ST pro—filtered before being modulated with a pulse shaping
`
`l’unctiOn. translated to the passband via parallel RF chains and then radiated frOm MT
`
`antennas. These signals pass through the radio channel where they are attenuated and
`
`undergo fading in multiple dimensions before they arrive at the MR receive antennas.
`
`Additive thermal noise in the M R parallel RFchains at the receiver corrupts the received
`
`signal. The mixture of signal plus noise is matched-filtered and sampled to produce MR
`
`output streams. Some form of additional ST post—filtering may also be applied. These
`
`streams are then ST deinterleaved and ST decoded to produce the output data bits.
`
`The difference between a ST communication system and a conventional system
`
`comes from the use of multiple antennas, ST encoding/interleaving, ST pro-filtering
`
`and post—filtering and ST decoding/deinterleaving.
`
`We conclude this chapter with a briefoverview of the areas discussed in the remainder
`
`of this book. Chapter 2 overviews ST propagation. We deve10p a channel representation
`
`as a vector valued ST random field and derive multiple representations and statistical
`
`descriptions of ST channels. We also describe real world channel measurements and
`models.
`
`Chapter 3 introduces XIXO channels, derives channels from statistical ST channel
`descriptions, proposes general XIXO channel models and test channel models and ends
`with a discussion on XIXO channel estimation at the receiver and transmitter.
`
`Chapter 4 studies channel capacity of XIXO channels under a variety of conditions:
`channel known and unknown to the transmitter, general channel models and frequency
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