MIMO Transceiver Design for Multi-Antenna Communications over Fading Channels

170 Pages·2013·2.35 MB·English

MIMO Transceiver Design for Multi-Antenna
Communications over Fading Channels
SIMON JÄRMYR
Doctoral Thesis in Telecommunications
Stockholm, Sweden 2013
KTH Royal Institute of Technology
School of Electrical Engineering
Signal Processing Laboratory
ISBN 978-91-7501-696-2 SE-100 44 Stockholm, SWEDEN
Akademisk avhandling sommed tillstånd av Kungliga Tekniska högskolan
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examen i telekommunikation måndagen den 22 april 2013 klockan 10.15 i
hörsal E2, Lindstedtsvägen 3, Stockholm.
© Simon Järmyr, April 2013, except where otherwise stated.
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Abstract
In wireless communications, the use of multiple antennas for both transmission
and reception is associated with performance gains of fundamental nature. One
such gain stems from the spatial-multiplexing capabilities of wireless multiple-
inputmultiple-output(MIMO)channels: Manypropagationenvironmentsadmit
several data streams to be conveyed in parallel over a single point-to-point link,
setting the stage for significantly increased data rates. The optimal maximum-
likelihood (ML) receiver pays a high price for spatial multiplexing in the form
of a heavy computational burden. Another receiver candidate is the decision-
feedback(DF)equalizer,reapingthebenefitsofspatialmultiplexingwithfarmore
efficient receive processing. Its combination of independent data-stream decod-
ingwith successive interference cancellation (SIC) is sufficient to achieveseveral
information-theoretic performance limits, both for point-to-point and multiple-
access channels. Nevertheless, in practical systems there is often a clear per-
formance degradation associated with DF processing compared to ML. In this
context,linearprecodingisatransmitpre-processingtechniquethatcanenhance
performance by adapting the transmission to available channel-state informa-
tion (CSI).
ThisthesisdealswithMIMOtransceiverdesignforDFequalization: thejoint
optimization of transmit precoding and receive equalization. A crucial aspect is
theassumptionsmadeonthekindofCSIavailable. Thethesisconsidersapracti-
calcasewithlong-term,statisticalCSIatthetransmitterandperfectshort-term
CSI at the receiver. The thesis presents an optimization framework for MIMO
transceiverdesign,contributinginseveralrespects. Firstly,anumberofrelevant
performancemeasuresarepresented,andnovelexpressionsareprovidedforspa-
tially correlated MIMO channels. Secondly, it is shown that optimization with
respect to such performance measures can be cast as convex optimization prob-
lems,enablingefficientsolutionsingeneral. Thethesisalsoprovidesanin-depth
analysisofspecificoptimizationproblems,enablingveryefficientsolutions;novel
connectionsareestablished between MIMOtransceiver design,convex-hullalgo-
rithms, and submodular optimization. Thirdly, an extension to the multi-user
uplink is provided. The thesis considers not only the joint optimization of the
users’precoders,butalsojointoptimizationwiththedecodingorderemployedat
thereceiver. Thethesisshowshowtoaddresstheharddesignprobleminacom-
putationally efficient manner using alternating optimization between precoders
andthedecodingorder, which isobservedtoconvergefast with close tooptimal
performance.

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Acknowledgements
I am grateful to my supervisor Prof. Björn Ottersten for taking me on as a
Ph.D. student in the Signal Processing group. He knows how excellent research
is conducted, and has always encouraged me, watched over my progress, and
guided me with gentle means. The freedom he has given me—to explore the
problemsthatcaughtmycuriosity—hasmaderesearchadaily,excitingquestfor
answers.
I’m also grateful to Prof. Eduard Jorswieck for his solid support over the
years as a co-author, always providing me with excellent feedback and insights.
Hewasalsotheonethatintroducedmetothebeautifultheoryofmajorization—
an indispensable mathematical toolbox for solving many of theproblems in this
thesis. I’m also indebted to Prof. Mats Bengtsson, my co-advisor, for assisting
me in so many ways, ranging from explaining obscure details of optimization
theory to doing magical tricks in LATEX. I would also like to thank Dr. Svante
Bergman for sharing his skills in the art of MIMO transceiver design, leading
to exciting collaboration. I’ve always enjoyed the frequent discussions with Dr.
Emil Björnson, my wing-mate during years of office sharing. He draws such
lovely golden stars when reviewing my work. And I thank Alla Tarighati—
my new office mate—for bearing with meduring thislast intensephase, and for
alwaysmakingmelightenup. IalsothankAnnikaAugustssonandToveSchwarz
for taking such good care of the administration. And I thank all of you at the
Signal Processing and Communication Theory groups who have contributed to
thewarm, and stimulating working atmosphere that I havehighly appreciated.
Iwish toacknowledgethosewhohavehelpedmewith proof-readingpartsof
thethesis: RasmusBrandt,JinghongYang,EfthymiosTsakonas,EmilBjörnson,
David Hammarwall, and Bengt Samuelsson. I’m also grateful to Prof. Antonio
Pascual-Iserte for the time and effort spent on acting as opponent, and to the
gradingcommittee: Prof. KimmoKansanen,Prof. MichailMatthaiou,andProf.
Tobias Oechtering.
Last, but certainly not least, I’d like to mention family and friends that
mean so much to me (perhaps more than you know). In particular, my loving
and ever-supporting parents Ingrid and Bengt have always been there for me
(even proof-reading every single page of the thesis). And my two brothers Joel
and David have always inspired me in various ways. I’m glad for my precious
son Melker, who has an amazing ability to make me put away what seems so
important,anddowhat’sreallyimportant: tojustcomeandplay. Andfinally,I
thankmywifeLinaforgenuinelybeingyou allalong,theoneI’vealwaysloved.
Simon Järmyr
Stockholm, March 2013

Contents
1 Introduction 1
1.1 The Wireless Channel . . . . . . . . . . . . . . . . . . . . . 1
1.2 Communication over Wireless Channels . . . . . . . . . . . 3
1.3 Designing MIMO Communication Systems . . . . . . . . . . 7
1.4 Aim of the Thesis. . . . . . . . . . . . . . . . . . . . . . . . 10
2 Problem Formulation and Contributions 11
2.1 The Wireless MIMO System Model . . . . . . . . . . . . . . 11
2.2 MIMO Transceiver Design . . . . . . . . . . . . . . . . . . . 16
2.3 Outline of Thesis Contributions . . . . . . . . . . . . . . . . 27
3 A Framework for Precoder Optimization 31
3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 Performance Measures . . . . . . . . . . . . . . . . . . . . . 34
3.3 Precoder Optimization . . . . . . . . . . . . . . . . . . . . . 43
3.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . 52
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.A A Collection of Proofs . . . . . . . . . . . . . . . . . . . . . 58
4 Performance Measures in Correlated MIMO Channels 65
4.1 SNR Characterization . . . . . . . . . . . . . . . . . . . . . 65
4.2 Performance Under the Generalized Gamma Distribution . 67
4.3 SNR Approximations for ZF Receivers . . . . . . . . . . . . 74
4.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . 77
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.A A Collection of Proofs . . . . . . . . . . . . . . . . . . . . . 81
5 Optimization with Majorization Constraints 87
5.1 Optimization with Skewed Majorization Constraints . . . . 87
5.2 Optimization with Submodular Constraints . . . . . . . . . 94
5.3 Statistical Precoding for ZF-DF Equalization . . . . . . . . 98
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.A A Convex-Hull Algorithm . . . . . . . . . . . . . . . . . . . 104
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5.B A Collection of Proofs . . . . . . . . . . . . . . . . . . . . . 105
6 Precoding and Ordering in the Multi-User Uplink 109
6.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 109
6.2 User Utility Functions . . . . . . . . . . . . . . . . . . . . . 114
6.3 Utility Regions . . . . . . . . . . . . . . . . . . . . . . . . . 116
6.4 Maximizing System Utility . . . . . . . . . . . . . . . . . . 120
6.5 Efficient Single-Mode Selection . . . . . . . . . . . . . . . . 123
6.6 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . 130
6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
6.A A Collection of Proofs . . . . . . . . . . . . . . . . . . . . . 137
7 Conclusions and Future Work 141
7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 142
Nomenclature 145
Bibliography 149