Highly Efficient Resource Allocation Techniques in 5G for NOMA-based Massive MIMO and

108 Pages·2017·2.13 MB·English

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Xin Liu, The University of Western Ontario
Supervisor: Xianbin Wang, The University of Western Ontario
A thesis submitted in partial fulfillment of the requirements for the Master of Engineering
Science degree in Electrical and Computer Engineering
© Xin Liu 2016
Follow this and additional works at: https://ir.lib.uwo.ca/etd
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RReeccoommmmeennddeedd CCiittaattiioonn
Liu, Xin, "Highly Efficient Resource Allocation Techniques in 5G for NOMA-based Massive MIMO and
Relaying Systems" (2016). Electronic Thesis and Dissertation Repository. 4136.
https://ir.lib.uwo.ca/etd/4136
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Abstract
The explosive proliferation of smart devices in the 5-th generation (5G) network expects
1,000-fold capacity enhancement, leading to the urgent need of highly resource-efficient tech-
nologies. Non-orthogonal multiple access (NOMA), a promising spectral efficient technology
for 5G to serve multiple users concurrently, can be combined with massive multiple input
multipleoutput(MIMO)andrelayingtechnology,toachievehighlyefficientcommunications.
Hence, this thesis studies the design and resource allocation of NOMA-based massive MIMO
andrelayingsystems.
Due to hardware constraints and channel condition variation, the first topic of the thesis
developsefficientantennaselectionanduserschedulingalgorithmsforsumratemaximization
intwoMIMO-NOMAscenarios. Inthesingle-bandscenario,theproposedalgorithmimproves
antenna search efficiency by limiting the candidate antennas to those are beneficial to the rel-
evant users. In the multi-band scenario, the proposed algorithm selects the antennas and users
with the highest contribution total channel gain. Numerical results show that our proposed
algorithmsachievesimilarperformancetootheralgorithmswithreducedcomplexity.
The second part of the thesis proposes the relaying and power allocation scheme for the
NOMA-assisted relaying system to serve multiple cell-edge users. The relay node decodes
its own message from the source NOMA signal and transmits the remaining part of signal to
cell-edge users. The power allocation scheme is developed by minimizing the system outage
probability. To further evaluate the system performance, the ergodic capacity is approximated
by analyzing the interference at cell-edge users. Numerical results proves the performance
improvementoftheproposedsystemoverconventionalorthogonalmultipleaccessmechanism.
Keywords: 5G;MassiveMIMO;NOMA;Relaying;ResourceAllocation
ii
Acknowlegements
The completion of this thesis involves the contribution and supports by a great number of
people, thanks to whom my graduate study is a valuable and unforgettable experience in my
life.
I would like to express my deep gratitude for Prof. Xianbin Wang for offering me the
opportunity to study in the University of Western Ontario. His enlightening supervision and
foresightmotivatedmetoidentifythecutting-edgeresearchtopics,developin-depthideasand
achieve efficient research progress. His kind patience also helps my professional development
through exploration of different research methods. The research and communication ability I
learnedfromhimwillsignificantlybenefitmyfutureworkandlife.
Iwouldalsothankmycolleagues,Dr. AydinBehnadandDr. GuanghuiSong,thepostdoc-
toralfellowsintheresearchgroupfortheirwarm-heartedhelpwithsomecriticalmathematical
and technical details in my research. I gained lots of technical knowledge by discussing with
themfrequently.
Additionally, many thanks to my research group, friends from other groups, faculty and
staff members of University of Western Ontario who have helped me with my study or life so
thatIwasabletoovercomevariousdifficultiesandfinallycompletedthisthesis.
EventuallyIwouldliketodemonstratemyspecialthankstomybelovedfatherandmother.
They will always support me with any physical and spiritual emotional support they can pro-
vide.
iii
Contents
Abstract ii
Acknowlegements iii
ListofFigures vi
ListofTables vii
ListofAppendices viii
ListofAbbreviations,Symbols,andNomenclature ix
1 Introduction 1
1.1 Backgroundof5G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 ResearchMotivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 AdvantagesofSpectralandPowerEfficientTechnologies . . . . . . . . 3
1.2.2 ChallengesforResourceAllocation . . . . . . . . . . . . . . . . . . . 5
1.3 ResearchObjectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5 ThesisOutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 TechnologiesforEfficientUtilizationofResourcesin5G 13
2.1 PrinciplesofResource-Efficienttechnologiesin5G . . . . . . . . . . . . . . . 13
2.1.1 NOMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
DrawbacksofConventionalOMA . . . . . . . . . . . . . . . . . . . . 14
NOMAAdvantagesoverOMAandNOMAPrinciple . . . . . . . . . . 15
NOMAPerformanceinTwo-userCase . . . . . . . . . . . . . . . . . . 18
2.1.2 MassiveMIMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1.3 RelayingTechnology . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2 ChallengesofUtilizingNOMA,MIMOandRelays . . . . . . . . . . . . . . . 23
2.2.1 UserSchedulingandPowerAllocationinNOMA . . . . . . . . . . . . 23
2.2.2 AntennaSelectionandUserSchedulinginMassiveMIMO . . . . . . . 24
2.2.3 NOMAAssistedRelayingSystemDesign . . . . . . . . . . . . . . . . 25
2.3 ConsiderationsonNOMAUserPairing . . . . . . . . . . . . . . . . . . . . . 26
2.3.1 UserPairingwithFixedAllocatedPower . . . . . . . . . . . . . . . . 26
2.3.2 UserPairingwithConsiderationofTargetRate . . . . . . . . . . . . . 27
2.4 ConsiderationsonNOMAPowerAllocation . . . . . . . . . . . . . . . . . . . 28
iv
2.4.1 SumRateMaximization . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4.2 FairnessConsideration . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 AntennaSelectionAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.1 AntennaSelectionandUseSchedulingBasedonExhaustiveSearch . . 30
2.5.2 AntennaSelectionandUseSchedulingBasedonSuccessiveElimination 33
2.6 CurrentDesignsforNOMA-basedRelayingSystem . . . . . . . . . . . . . . . 34
2.6.1 SystemwithSingleRelay . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.6.2 SystemwithMultipleRelayDevices . . . . . . . . . . . . . . . . . . . 36
2.7 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 EfficientAntennaSelectionandUserSchedulingin5GMassiveMIMO-NOMA
System 39
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 SystemModelandProblemFormulation . . . . . . . . . . . . . . . . . . . . . 43
3.2.1 MassiveMIMO-NOMASystemModel . . . . . . . . . . . . . . . . . 43
3.2.2 ProblemFormulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3 AntennaSelectioninSingle-bandTwo-userScenario . . . . . . . . . . . . . . 45
3.3.1 PowerAllocationScheme . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.2 EfficientSearchAlgorithmforAntennaSelection . . . . . . . . . . . . 47
3.4 JointAntennaSelectionandUserSchedulinginMulti-bandMulti-userScenario 49
3.5 NumericalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.6 Discussion: PracticalExecutionofProposedAlgorithm . . . . . . . . . . . . . 58
3.7 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4 Power Allocation and Performance of Collaborative NOMA Assisted Relaying
System 60
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.2 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 PowerAllocationandPerformance . . . . . . . . . . . . . . . . . . . . . . . . 69
4.3.1 PowerAllocationSchemeandOutagePerformance . . . . . . . . . . . 70
4.3.2 ErgodicCapacityPerformance . . . . . . . . . . . . . . . . . . . . . . 75
4.4 NumericalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.5 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5 Conclusions 84
5.1 ThesisSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.2 FutureWorks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Bibliography 87
A ProofsofEquationsforPerformanceAnalysisinCNARSystem 94
B AnalysisofComplexityforsingle-bandScenario 97
CurriculumVitae 98
v
List of Figures
1.1 5Gusecasesandcorrespondingrequirements. . . . . . . . . . . . . . . . . . . 3
2.1 ProtocoldifferencebetweenOMAandNOMA. . . . . . . . . . . . . . . . . . 16
2.2 GeneralcaseofSICmechanismforNOMA. . . . . . . . . . . . . . . . . . . . 16
2.3 AchievablecapacityunderNOMAprotocol. . . . . . . . . . . . . . . . . . . . 19
2.4 DepictionofmassiveMIMOsystem. . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Illustrationofantennaselectionanduserscheduling. . . . . . . . . . . . . . . 24
2.6 NOMA-basedRelayingSystemwithSingleRelay. . . . . . . . . . . . . . . . 36
2.7 NOMA-assistedRelayingSystemwithMultipleRelays. . . . . . . . . . . . . . 37
3.1 MassiveMIMO-NOMAsystemwithantennaselectionanduserscheduling. . . 43
3.2 Userserviceink-thsubbandofmassiveMIMO-NOMAsystem. . . . . . . . . 44
3.3 AcontributionupdateexampleofjointAUcontributionalgorithm. . . . . . . . 52
3.4 Sum rate and outage probability as functions of minimum required PSNR in
single-bandscenariowhere M = 18, L = 6. . . . . . . . . . . . . . . . . . . 53
T T
3.5 Sum rate and outage probability as functions of candidate antenna number in
single-bandscenariowheret = 9, L = 6. . . . . . . . . . . . . . . . . . . . . 54
T
3.6 Performance of sum rate and outage probability as functions of minimum re-
quiredPSNRinmulti-bandscenariowhere M = 10, L = 6, K = 3. . . . . . . 56
R T
3.7 Performance of sum rate and outage probability as functions of candidate user
numberinmulti-bandscenariowheret = 10, L = 6, K = 3. . . . . . . . . . . 57
T
4.1 CNARsystemmodel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.2 DecodingprocessatMT1forthefirstphasebasedonSIC. . . . . . . . . . . . 66
4.3 SIC-based decoding process at NOMA far user and near user for the second
phase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.4 IllustrationofconventionalOMAsystem. . . . . . . . . . . . . . . . . . . . . 77
4.5 DepictionofOMA-basedrelayingsystem. . . . . . . . . . . . . . . . . . . . . 77
4.6 PerformanceofOutageprobabilityasafunctionoftargetrateR whenρ = 15dB. 79
0 2
4.7 OutageprobabilityasafunctionofBStransmitSNRρ whenR = 2. . . . . . 79
1 0
4.8 OutageprobabilityasafunctionofMT1transmitSNRρ whenR = 2. . . . . 81
2 0
4.9 Single-userergodiccapacityasafunctionofMT1transmitSNRρ whenR =
2 0
2andρ = 19dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
1
4.10 Sum ergodic capacity as a function of MT1 transmit SNR ρ when R = 2 and
2 0
ρ = 19dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
1
vi
List of Tables
4.1 Tableforvalueofmin{SNR ,SNR }underdifferentconditions . . . . . . . 76
2,1 2,2
4.2 Alistforvalueofmin{SNR ,SNR }indifferentconditions . . . . . . . . . 76
3,1 3,2
vii
List of Appendices
AppendixAProofsofEquationsforPerformanceAnalysisinCNARSystem . . . . . . 94
AppendixBAnalysisofComplexityforsingle-bandScenario . . . . . . . . . . . . . . . 97
viii
Abbreviations
5G The5-thGenerationNetwork
AF Amplify-and-Forward
AWGN AdditiveWhiteGaussianNoise
BS BaseStation
CNAR CollaborativeNOMAAssistedRelayingSystem
D2D Device-to-Device
DF Decode-and-Forward
IoT InternetofThings
MIMO Multi-InputMulti-Output
mmWave millimeter-wave
MRC MaximalRatioCombing
MT MobileTerminal
MU-MIMO Multi-UserMulti-InputMulti-Output
NOMA Non-orthogonalMultipleAccess
OFDMA OrthogonalFrequencyDivisionMultipleAccess
OMA OrthogonalMultipleAccess
PSNR Post-processingSignal-to-NoiseRatio
R-D Relay-Destination
RF RadioFrequency
S-R Source-Relay
SIC SuccessiveInterferenceCancellation
ix
SNR Signal-to-NoiseRatio
SU-MIMO Single-UserMulti-InputMulti-Output
TDMA TimeDivisionMultipleAccess
x