Compressive Sensing for Wireless Networks

310 Pages·2013·6.007 MB·English

by  Zhu Han, Husheng Li, Wotao Yin

more information - www.cambridge.org/9781107018839

CompressiveSensingforWirelessNetworks
Compressive sensing is a new signal-processing paradigm that aims to encode sparse
signalsbyusingfarlowersamplingratesthanthoseinthetraditionalNyquistapproach.
It helps acquire, store, fuse and process large data sets efficiently and accurately. This
method,whichlinksdataacquisition,compression,dimensionalityreduction,andopti-
mization, has attracted significant attention from researchers and engineers in various
areas. This comprehensive reference develops a unified view on how to incorporate
efficiently the idea of compressive sensing over assorted wireless network scenarios,
interweaving concepts from signal processing, optimization, information theory, com-
munications, and networking to address the issues in question from an engineering
perspective.Itenablesstudents,researchers,andcommunicationsengineerstodevelop
a working knowledge of compressive sensing, including background on the basics of
compressive sensing theory, an understanding of its benefits and limitations, and the
skillsneededtotakeadvantageofcompressivesensinginwirelessnetworks.
ZhuHanisanAssociateProfessorintheElectricalandComputerEngineeringDepart-
mentattheUniversityofHouston,Texas.HereceivedanNSFCAREERawardin2010
and the IEEE Fred W. Ellersick Prize in 2011. He has co-authored papers that won
the best paper award at the IEEE International Conference on Communications 2009,
the 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc,
andWirelessNetworks(WiOpt09),theIEEEWirelessCommunicationandNetworking
Conference,2012andIEEESmartgridcommConference,2012.
HushengLiisanAssistantProfessorintheElectricalandComputerEngineeringDepart-
mentattheUniversityofTennessee.HereceivedtheBestPaperAwardoftheEURASIP
JournalonWirelessCommunications andNetworkingin2005(togetherwithhisPhD
advisor,ProfessorH.V.Poor),theBestDemoAwardofIEEEGlobecomin2010,and
theBestPaperAwardatIEEEICCin2011.
WotaoYin is an Associate Professor at the Department of Computational and Applied
MathematicsatRiceUniversity.HewonanNSFCAREERawardin2008andanAlfredP.
SloanResearchFellowshipin2009.

Compressive Sensing for
Wireless Networks
ZHU HAN
UniversityofHouston,USA
HUSHENG LI
UniversityofTennessee,USA
WOTAO YIN
RiceUniversity,USA
CAMBRIDGEUNIVERSITYPRESS
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Informationonthistitle:www.cambridge.org/9781107018839
(cid:2)C CambridgeUniversityPress2013
Thispublicationisincopyright.Subjecttostatutoryexception
andtotheprovisionsofrelevantcollectivelicensingagreements,
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permissionofCambridgeUniversityPress.
Firstpublished2013
PrintedandboundintheUnitedKingdombytheMPGBooksGroup
AcataloguerecordforthispublicationisavailablefromtheBritishLibrary
LibraryofCongressCataloguinginPublicationdata
Han,Zhu,1974–
Compressivesensingforwirelessnetworks/ZhuHan,UniversityofHouston,USA,HushengLi,University
ofTennessee,USA,WotaoYin,RiceUniversity,USA.
pages cm
Includesbibliographicalreferencesandindex.
ISBN978-1-107-01883-9(hardback)
1.Codingtheory. 2.Datacompression(Telecommunication) 3.Signalprocessing–Digitaltechniques.
4.Sampling(Statistics) I.Li,Husheng,1975– II.Yin,Wotao. III.Title.
TK5102.92.H355 2013
621.39(cid:3)81–dc23 2013000272
ISBN 978-1-107-01883-9Hardback
CambridgeUniversityPresshasnoresponsibilityforthepersistenceor
accuracyofURLsforexternalorthird-partyinternetwebsitesreferredto
inthispublication,anddoesnotguaranteethatanycontentonsuch
websitesis,orwillremain,accurateorappropriate.
ForthepeopleImetintheBarneoicecamp,NorthPole,whoshowedme
thebraverytoconqueranydifficulty,whichencouragedmetofinishthis
challengingbook ZhuHan
Tomywife,MinDuan,andmyson,SiyiLi HushengLi
Tothosewhoadvocateforintellectualhonestyanddefendacademicintegrity
WotaoYin

Contents
Preface pagexiii
1 Introduction 1
1.1 Motivationandobjectives 1
1.2 Outline 2
2 Overviewofwirelessnetworks 6
2.1 Wirelesschannelmodels 6
2.1.1 Radiopropagation 6
2.1.2 Interferencechannel 11
2.2 Categorizationofwirelessnetworks 13
2.2.1 3Gcellularnetworksandbeyond 13
2.2.2 WiMAXnetworks 17
2.2.3 WiFinetworks 19
2.2.4 Wirelesspersonalareanetworks 22
2.2.5 Wirelessadhocnetworks 28
2.2.6 Wirelesssensornetworks 32
2.3 Advancedwirelesstechnology 36
2.3.1 OFDMtechnology 36
2.3.2 Multipleantennasystem 39
2.3.3 Cognitiveradios 41
2.3.4 Schedulingandmultipleaccess 43
2.3.5 Wirelesspositioningandlocalization 45
PartI CompressiveSensingTechnique
3 Compressivesensingframework 51
3.1 Background 51
3.2 Traditionalsensingversuscompressivesensing 56
3.3 Sparserepresentation 57
3.3.1 Extensionsofsparsemodels 59
viii Contents
3.4 CSencodinganddecoding 60
3.5 Examples 67
4 Sparseoptimizationalgorithms 69
4.1 Abriefintroductiontooptimization 70
4.2 Sparseoptimizationmodels 73
4.3 Classicsolvers 74
4.4 Shrinkageoperation 76
4.4.1 Generalizationsofshrinkage 78
4.5 Prox-linearalgorithms 79
4.5.1 Forward-backwardoperatorsplitting 80
4.5.2 Examples 81
4.5.3 Convergencerates 83
4.6 Dualalgorithms 83
4.6.1 Dualformulations 84
4.6.2 TheaugmentedLagrangianmethod 85
4.6.3 Bregmanmethod 86
4.6.4 Bregmaniterationsanddenoising 88
4.6.5 LinearizedBregmanandaugmentedmodels 90
4.6.6 Handlingcomplexdataandvariables 92
4.7 Alternatingdirectionmethodofmultipliers 93
4.7.1 Framework 94
4.7.2 ApplicationsofADMinsparseoptimization 96
4.7.3 Applicationsindistributedoptimization 100
4.7.4 Applicationsindecentralizedoptimization 102
4.7.5 Convergencerates 102
4.8 (Block)coordinateminimizationandgradientdescent 103
4.9 Homotopyalgorithmsandparametricquadraticprogramming 105
4.10 Continuation,varyingstepsizes,andlinesearch 107
4.11 Non-convexapproachesforsparseoptimization 109
4.12 Greedyalgorithms 110
4.12.1 Greedypursuitalgorithms 110
4.12.2 Iterativesupportdetection 112
4.12.3 Hardthresholding 113
4.13 Algorithmsforlow-rankmatrices 114
4.14 Howtochooseanalgorithm 115
5 CSanalog-to-digitalconverter 118
5.1 TraditionalADCbasics 118
5.1.1 Samplingtheorem 118
5.1.2 Quantization 120
5.1.3 Practicalimplementation 121