On Spectrum Sensing, Resource Allocation, and Medium Access Control in Cognitive Radio ...

197 Pages·2015·3.22 MB·English

On Spectrum Sensing, Resource Allocation, and Medium
Access Control in Cognitive Radio Networks
by
Madushan Thilina Karaputugala Gamacharige
A Thesis submitted to The Faculty of Graduate Studies of
The University of Manitoba
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Department of Electrical and Computer Engineering
University of Manitoba
Winnipeg
January 2015
Copyright c 2015byMadushanThilinaKaraputugalaGamacharige
(cid:13)
Abstract
Thecognitiveradio-basedwirelessnetworkshavebeenproposedasapromisingtechnology
to improve the utilization of the radio spectrum through opportunistic spectrum access. In
thiscontext,thecognitiveradiosopportunisticallyaccessthespectrumwhichislicensedto
primaryuserswhentheprimaryusertransmissionisdetectedtobeabsent. Foropportunis-
tic spectrum access, the cognitive radios should sense the radio environment and allocate
the spectrum and power based on the sensing results. To this end, in this thesis, I develop
a novel cooperative spectrum sensing scheme for cognitive radio networks (CRNs) based
on machine learning techniques which are used for pattern classification. In this regard,
unsupervised and supervised learning-based classification techniques are implemented for
cooperative spectrum sensing. Secondly, I propose a novel joint channel and power al-
location scheme for downlink transmission in cellular CRNs. I formulate the downlink
resourceallocationproblemasageneralizedspectral-footprintminimizationproblem. The
channel assignment problem for secondary users is solved by applying a modified Hun-
garian algorithm while the power allocation subproblem is solved by using Lagrangian
technique. Specifically,Iproposealow-complexitymodifiedHungarianalgorithmforsub-
channelallocationwhichexploitsthelocalinformationinthecostmatrix. Finally,Ipropose
a novel dynamic common control channel-based medium access control (MAC) protocol
for CRNs. Specifically, unlike the traditional dedicated control channel-based MAC pro-
tocols, the proposed MAC protocol eliminates the requirement of a dedicated channel for
controlinformationexchange.
Acknowledgements
This dissertation would not have even been possible without the help and dedication of
some special people in many ways. Undoubtedly, I would like to express my foremost
heartiest gratitude to my dissertation advisor Professor Ekram Hossain for his excellent
guidance, encouragement and dedication that pushed me far beyond my expectation. Spe-
cially,nomatterhowbusyhewas,hecouldalwaysfindtimetodiscussions. Thankyoufor
allofthat!
I gratefully acknowledge the members of the dissertation examination committee Pro-
fessorPradeepaYahampath,ProfessorPourangIraniandProfessorAbrahamFapojuwofor
their constructive criticism, valuable comments and suggestions on this study. My spe-
cial thank also should goes to other faculty staffs for their collaboration for improving my
knowledge in numerous ways. I make this an opportunity to acknowledge the financial
supportfromUMGFandNSERCtocarryoutmyPHD.Specially,Iwouldliketothankall
mygroupmembers,labcolleaguesandtheSriLankancommunityinWinnipegforallkind
ofsupportsgiventomeduringmystudy.
No single piece of this dissertation would be a reality without the seamless support of
my parents, Hemalatha and Dharmasena, who always encourage my dreams and aspira-
tions no matter which direction I chose. I would have never been in this position without
their love, care, dedications and support. The minimum I can give back is to dedicate this
dissertationtomyparents.
Iwouldliketoacknowledgemysisters,SamanthiandKeshala,andmybrother-in-law,
Lakmal, for providing constant support and keeping me in good mood by taking care of
everythingbackhomeonmybehalf.
Most importantly, I have no word to express my gratitude to my loving wife, Saranga.
Her continuous understanding, support, encouragement, patience and proof reading made
i
my life easy. Her love and care makes anywhere we live together called home. Nothing is
possiblewithoutherdedication. Thankyouforallofthat!
ii
Table of Contents
ListofFigures vii
ListofTables ix
ListofAbbreviations x
ListofSymbols xiii
Publications xvii
1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 BasicsofCognitiveRadio: MotivationandDefinition . . . . . . . . 2
1.1.2 ChallengesinCognitiveRadioNetworks . . . . . . . . . . . . . . 3
1.2 ResearchGoal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 ResearchContributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 OrganizationoftheThesis . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 OverviewonCognitiveRadioNetworks 11
2.1 RegulatoryAspectsandStandardization . . . . . . . . . . . . . . . . . . . 12
2.2 CognitiveRadioNetworkArchitectures . . . . . . . . . . . . . . . . . . . 12
2.2.1 CentralizedCognitiveRadioNetworks . . . . . . . . . . . . . . . 13
2.2.2 DistributedCognitiveRadioNetworks . . . . . . . . . . . . . . . . 14
2.2.3 CROperationalModels . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.4 SpectrumAccessModel . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 SpectrumManagementFramework . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 SpectrumSensing . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2 SpectrumDecision . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.3 SpectrumSharing . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.4 SpectrumMobility . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 SpectrumSensingTechniques . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 Non-CooperativeSpectrumSensing . . . . . . . . . . . . . . . . . 20
2.4.2 CooperativeSpectrumSensing . . . . . . . . . . . . . . . . . . . . 23
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TableofContents
2.4.3 FusionTechniquesforCooperativeSpectrumSensing . . . . . . . 24
2.4.4 PatternClassificationTechniquesforCooperativeSpectrumSensing 25
2.4.5 DatabaseCentricApproachforSpectrumSensing . . . . . . . . . . 27
2.5 ResourceAllocationinCRNs . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.5.1 ElementsofResourceAllocationProblemsinCRNs. . . . . . . . . 28
2.5.2 ResourceAllocationApproachesinCognitiveRadioNetworks . . . 29
2.6 MediumAccessControlforCognitiveRadioNetworks . . . . . . . . . . . 32
2.6.1 GeneralCognitiveMediumAccessControl(C-MAC)Cycle . . . . 34
3 MachineLearningTechniquesforCooperativeSpectrumSensinginCognitive
RadioNetworks 40
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.1.1 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.1.2 MotivationandContribution . . . . . . . . . . . . . . . . . . . . . 42
3.2 SystemModelandAssumptions . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.1 CognitiveRadioNetworkandPrimaryUserModel . . . . . . . . . 44
3.2.2 EnergyVectorModel . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3 MachineLearning-BasedCooperativeSpectrumSensingFramework . . . . 47
3.3.1 OperationofProposedCSSFramework . . . . . . . . . . . . . . . 47
3.3.2 AdvantagesofProposedCSSFramework . . . . . . . . . . . . . . 49
3.4 UnsupervisedLearningforCooperativeSpectrumSensing . . . . . . . . . 53
3.4.1 MotivationforUnsupervisedLearning . . . . . . . . . . . . . . . . 53
3.4.2 K-MeansClusteringAlgorithm . . . . . . . . . . . . . . . . . . . 55
3.4.3 GaussianMixtureModel . . . . . . . . . . . . . . . . . . . . . . . 56
3.5 SupervisedLearningforCooperativeSpectrumSensing . . . . . . . . . . . 60
3.5.1 MotivationforSupervisedLearning . . . . . . . . . . . . . . . . . 60
3.5.2 SupportVectorMachine . . . . . . . . . . . . . . . . . . . . . . . 61
3.5.3 WeightedK-Nearest-Neighbor . . . . . . . . . . . . . . . . . . . . 65
3.6 PerformanceEvaluationandDiscussion . . . . . . . . . . . . . . . . . . . 66
3.6.1 SimulationParameters . . . . . . . . . . . . . . . . . . . . . . . . 66
3.6.2 TrainingDurationforDifferentClassifiers . . . . . . . . . . . . . . 67
3.6.3 AverageClassificationDelayforDifferentClassifiers . . . . . . . . 68
3.6.4 DetectionProbabilityforDifferentClassifiers . . . . . . . . . . . . 69
3.6.5 SummaryofResults . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4 A Dynamic Common Control Channel-Based MAC Protocol for Cellular
CRNs 74
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.1.1 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.1.2 MotivationandContribution . . . . . . . . . . . . . . . . . . . . . 76
4.2 SystemModelandAssumptions . . . . . . . . . . . . . . . . . . . . . . . 79
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TableofContents
4.3 DynamicCCC-BasedMediumAccessControlProtocol . . . . . . . . . . . 81
4.3.1 SpectrumSensingPhase . . . . . . . . . . . . . . . . . . . . . . . 81
4.3.2 CCCSelectionPhase . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.3.3 DataTransmissionPhase . . . . . . . . . . . . . . . . . . . . . . . 87
4.3.4 BeaconPhase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.4 CooperativeSpectrumSensingandSelectionofCCC . . . . . . . . . . . . 89
4.4.1 CalculationoftheMinimumNumberofMini-SlotsRequiredforat
¯
leastN SUstocooperateinspectrumsensing,N . . . . . . . . . . 90
b
4.4.2 SVM-BasedMethodforSelectionoftheCCC . . . . . . . . . . . . 93
4.4.3 SelectionofChannelsforSpectrumSensing . . . . . . . . . . . . . 94
4.5 SaturationThroughputAnalysisoftheDCCC-MACProtocol . . . . . . . . 94
4.6 RobustnessandScalabilityofDCCC-MACprotocol . . . . . . . . . . . . 97
4.6.1 A new SU appears in the CRN or an SU is not synchronized with
beacon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.6.2 Participate in the CCC selection process but does not receive the
CCCinformation . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.6.3 TheselectedCCCmaybeoccupiedbyPUs . . . . . . . . . . . . . 100
4.6.4 Adaptationoftheprotocolfordownlinktransmission . . . . . . . . 101
4.7 PerformanceEvaluationResultsandDiscussions . . . . . . . . . . . . . . 102
4.7.1 SimulationParameters . . . . . . . . . . . . . . . . . . . . . . . . 102
4.7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5 GeneralizedSpectralFootprintMinimizationforOFDMA-BasedCRNs 112
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.1.1 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.1.2 MotivationandContribution . . . . . . . . . . . . . . . . . . . . . 114
5.2 SystemModelandProblemFormulation . . . . . . . . . . . . . . . . . . . 116
5.2.1 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.2.2 ProblemFormulationforJointSubchannelandPowerAllocation . 117
5.3 SolutionsoftheSubchannelandPowerAllocationProblems . . . . . . . . 120
5.3.1 SubchannelAllocationProblem . . . . . . . . . . . . . . . . . . . 120
5.3.2 PowerAllocation . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.3.3 CompleteResourceAllocationAlgorithm . . . . . . . . . . . . . . 130
5.4 SpectralFootprintAnalysisforaSingleUserScenario . . . . . . . . . . . 131
5.5 SimulationResultsandDiscussions . . . . . . . . . . . . . . . . . . . . . 135
5.5.1 SimulationParameters . . . . . . . . . . . . . . . . . . . . . . . . 135
5.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
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TableofContents
6 ConclusionandFutureDirections 142
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6.2 FutureDirections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
6.2.1 ImprovedCooperativeSpectrumSensing . . . . . . . . . . . . . . 145
6.2.2 ExtensionoftheGeneralizedSpectralFootprintMinimizationModel146
6.2.3 ExtensionoftheDCCC-MACProtocol . . . . . . . . . . . . . . . 148
6.2.4 Full-DuplexCognitiveRadioNetworks . . . . . . . . . . . . . . . 149
A AppendixA 172
A.1 Proof of Optimality of Algorithm 4 for a given number of subchannel re-
quirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
A.1.1 ProofforCaseI: . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
A.1.2 ProofforCaseII: . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
A.1.3 ProofforCaseIII: . . . . . . . . . . . . . . . . . . . . . . . . . . 176
A.2 ComplexityofAlgorithm4foragivennumberofsubchannelrequirement 177
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List of Figures
2.1 Cognitive radio operational models. a) Underlay operation b) Overlay op-
erationc)Interweaveoperation. . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 SpectrummanagementframeworkinCRNs[1]. . . . . . . . . . . . . . . . 16
2.3 Spectrumdecisionflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4 Perfectly separable hyperplane for two class training data, where w is the
weightingvectorandw isthebias. . . . . . . . . . . . . . . . . . . . . . 25
0
2.5 GeneralcognitiveMACcycle. . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1 ModulararchitectureoftheproposedCSSframework. . . . . . . . . . . . 49
3.2 Twoscenariosofuserlocations. . . . . . . . . . . . . . . . . . . . . . . . 51
3.3 Examplescatterplotsofenergyvectorsintwoscenarios. . . . . . . . . . . 52
3.4 TheCRnetworktopologyusedforsimulation. . . . . . . . . . . . . . . . . 67
3.5 The ROC curves when a single PU is present. I use 500 training energy
vectorstotraineachclassifier. . . . . . . . . . . . . . . . . . . . . . . . . 70
3.6 TheROCcurveswhentherearetwoPUs. Iuse500trainingenergyvectors
totraineachclassifier. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.7 The detection probability according to the transmission power of a PU
whenthefalsealarmprobabilityis0.1andthereare25(5 5)SUs. . . . . . 72
×
4.1 FramestructureoftheDCCC-MACprotocol. . . . . . . . . . . . . . . . . 82
4.2 TheoperationoftheDCCC-MACprotocol. . . . . . . . . . . . . . . . . . 83
4.3 Minimum number of mini-slots (N ) required for at least 4 or more SUs
b
participating in the cooperative decision making process when P = 0.4
s
andW = 16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
max
¯
4.4 NumberofSUsparticipating(N )inthecooperativedecisionmakingpro-
N
b
cesswithinN mini-slotswhenP = 0.4andW = 16. . . . . . . . . . . 105
b s max
4.5 TheROCperformancefordifferentcombinationofSUs’cooperationwhen
therearethreePUsandtheSVMclassifierwithlinearkernelisused. . . . . 106
4.6 Effect of sensing duration (τ) on the performance of cooperative decision
making process when there are three PUs, four SUs and a linear kernel is
usedinSVMclassifier. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
vii
ListofFigures
4.7 SystemthroughputvariationwithrespecttothenumberofSUswhenP =
s
0.4 and W = 16. The overhead of the OSA-MAC protocol is assumed
max
tobe25%[2]andaveragetimeformobilitysupportinCM-MACprotocol
isassumedtobe3ms[3]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.8 System throughput variation with respect to contention window (W )
max
¯
whenP = 0.4and = 32. . . . . . . . . . . . . . . . . . . . . . . . . . 109
s
K
4.9 Effect of sensing duration (τ) on the performance of system throughput
¯
whenW = 16, = 32andtheframedurationis50ms). . . . . . . . . 110
max
K
5.1 Systemmodel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.2 Blockdiagramoftheresourceallocationprocess. . . . . . . . . . . . . . . 120
5.3 Algorithmstepstoupdatethenumberofsubchannels. . . . . . . . . . . . . 126
5.4 BehaviourofΩ(see(5.18))whenthereisasingleuserinthenetwork. . . 135
5.5 Average number of subchannel requirement when there are 4 SUs in the
network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.6 Averagetransmitpowerofuserswhenthereare4SUsinthenetwork. . . . 138
5.7 Spectralfootprintwhenthereare4SUsinthenetwork. . . . . . . . . . . . 138
5.8 Convergenceoftheproposedresourceallocationalgorithm. . . . . . . . . . 139
5.9 Comparisonoftheproposedresourceallocationalgorithmwiththeoptimal
resource allocation algorithm based on simulations when N = 2, = 5,
K
ω = [0.2, 0.3, 0.4, 0.5, 0.6],α = 1andβ = 1. . . . . . . . . . . . . . . 140
k
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