Algorithms for 5G physical layer

225 Pages·2017·7.09 MB·English

by  Pawan Dhakal

Politecnico di Torino
Porto Institutional Repository
[Doctoral thesis] Algorithms for 5G physical layer
Original Citation:
Pawan Dhakal (2017). Algorithms for 5G physical layer. PhD thesis
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DOI:10.6092/polito/porto/2670627
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DoctoralDissertation
DoctoralPrograminElectronicsEngineering(29thcycle)
Algorithms for 5G Physical Layer
By
Pawan Dhakal
******
Supervisor(s):
Prof. RobertoGarello,Supervisor
DoctoralExaminationCommittee:
Prof. PaolaPierleoni,UniversityofMarche,
Prof. FulvioBabich,UniversityofTrieste,
Prof. FrancescaVatta,UniversityofTrieste,
Prof. StefanoMangione,UniversityofPalermo
PolitecnicodiTorino
2017
Declaration
Iherebydeclarethat,thecontentsandorganizationofthisdissertationconstitutemy
ownoriginalworkanddoesnotcompromiseinanywaytherightsofthirdparties,
includingthoserelatingtothesecurityofpersonaldata.
PawanDhakal
2017
* This dissertation is presented in partial fulfillment of the requirements for Ph.D.
degreeintheGraduateSchoolofPolitecnicodiTorino(ScuDo).
Iwouldliketodedicatethisthesistomylovingparents,mywifeandmyson.
Acknowledgements
ItakeimmensepleasureinthankingDepartmentofElectronicsandTelecommuni-
cationEngineering,PolitecnicodiTorino,Italy,forprovidingmeanopportunityto
carryoutthisPhDResearchduringtheyears2014/2016.
Mydeepestgratitudetomyadvisor,Prof. RobertoGarello,fortheableguidance
and for his invaluable professional and human support in all possible ways ever
since.
Then, I am grateful to Prof. Anand Gachhadar for hosting me at Kathmandu
University, Nepal. Special thanks to all my former colleagues at Department of
Electrical and Electronics Engineering of Kathmandu University for the friendly
andwelcomingatmospherethatreallymademefeelathome. Furthermore,Iwant
tosincerelythankmyfriendandcolleagueDr. DanielRivielloforourfruitfuland
lasting collaboration, Mr. Research Dawadi for his help in the reviewing process.
SpecialthankstoAdrianaScavelloandFrancoMantionefortheirmoralsupportand
deepestconcern, andlastbutnotleast, myfamily andeveryone whosupportedme
duringtheseyears.
Abstract
There is a great activity in the researchcommunity towards the investigations of the
variousaspectsof5Gatdifferentprotocollayersandpartsofthenetwork. Among
all, physical layer design plays a very important role to satisfy high demands in
terms of data rates, latency, reliability and number of connected devices for 5G
deployment. This thesis addresses the latest developments in the physical layer
algorithms regarding the channel coding, signal detection, frame synchronization
andmultipleaccesstechniqueinthelightof5Gusecases. Thesedevelopmentsare
governed bythe requirements of thedifferent use case scenarios that are envisioned
tobethedrivingforcein5G.
Allchaptersfromchapter2to5aredevelopedaroundtheneedofphysicallayer
algorithmsdedicatedto5Gusecases. Inbrief,thisthesisfocusesondesign,analysis,
simulationandtheadvancementofphysicallayeraspectssuchas1. Reliabilitybased
decodingofshortlengthLinearBlockCodes(LBCs)withverygoodpropertiesin
termsofminimumhammingdistanceforverysmalllatencyrequiringapplications. In
thiscontext, weenlargethe gridof possiblecandidatesby considering,in particular,
short length LBCs (especially extended BCH codes) with soft-decision decoding;
2. Efficient synchronization of preamble/postamble in a short bursty frame using
modified Massey correlator; 3. Detection of Primary User activity using semi-
blindspectrumsensingalgorithmsand analysisofsuchalgorithmsunderpractical
imperfections;4. DesignofoptimalspreadingmatrixforaLowDensitySpreading
(LDS)techniqueinthecontextofnon-orthogonalmultipleaccess. Insuchspreading
matrix, small number of elements in a spreading sequences are non zero allowing
eachusertospreaditsdataoversmallnumberofchips(tones),thussimplifyingthe
decodingprocedureusingMessagePassingAlgorithm(MPA).
Publicationss
Some of the contents of this thesis have previously appeared or will appear in the
followingpublications:
Book Chapter
1. [49] Pawan Dhakal, Shree K. Sharma, Symeon Chatzinotas, Björn Otter-
sten and Daniel Riviello, “Effect of Primary User Traffic on Largest Eigen-
valueBasedSpectrumSensingTechnique”,inCognitiveRadioOrientedWire-
lessNetworks,11thInternationalConference,CROWNCOM2016,Grenoble,
France,May30-June1,2016,Proceedings,SpringerInternationalPublish-
ing, pp. 67-78, 2016. ISBN: 978-3-319-40351-9, http://dx.doi.org/10.1007/
978-3-319-40352-6_6.
Journal
1. [22]M.BaldiandM.BertinelliandF.ChiaraluceandP.DhakalandR.Garello
and N. Maturo and E. Paolini, “A Study on State-of-the-Art Space Mission
TelecommandReceivers”,InIEEEAerospaceandElectronicSystemsMaga-
zine,2017. InPress.
Conference Proceedings
1. [48]PawanDhakal,RobertoGarello,ShreeKrishnaSharma,SymeonChatzino-
tasandBjornOttersten,“OntheErrorPerformanceBoundofOrderedStatis-
vii
ticsDecodingofLinearBlockCodes”,IEEEIntl. Conf. onCommun. 2016,
Jun2016,http://dx.doi.org/10.1109/ICC.2016.7510798.
2. [44]PawanDhakal,DanielRiviello,“Multi-AntennaEnergyDetectorUnder
UnknownPrimaryUserTraffic”,COCORA2016,TheFourthIntl. Conf. on
Advancesin Cognitive Radio, Feb. 2016. http://www.thinkmind.org/download.
php?articleid=cocora_2016_1_40_80032.
3. [146] Daniel Riviello, Pawan Dhakal, Roberto Garello, “Performance Anal-
ysis of Multi-Antenna Hybrid Detectors and Optimization with Noise Vari-
anceEstimation”,InternationalConferenceonAdvancesinCognitiveRadio
(COCORA),April2015,http://www.thinkmind.org/download.php?articleid=
cocora_2015_1_30_80029.
4. [147]DanielRiviello,Pawan Dhakal, RobertoGarello,“OntheUseofEigen-
vectorsinMulti-AntennaSpectrumSensingwithNoiseVarianceEstimation”,
2nd Intel. Conf. in Signal Processing and Integrated Networks, Feb. 2015,
http://dx.doi.org/10.1109/SPIN.2015.7095339.
5. [45] Pawan Dhakal, Daniel Riviello, Roberto Garello, “SNR Wall Analysis
of Multi-Sensor Energy Detection with Noise Variance Estimation”, 11th
IEEE Intl. Symp. on Wireless Commun. Systems (ISWCS), Aug. 2014,
http://dx.doi.org/10.1109/ISWCS.2014.6933440.
6. [46] Pawan Dhakal, Roberto Garello, Federico Penna and Daniel Riviello,
“ImpactofNoiseEstimationonEnergyDetectionandEigenvalueBasedSpec-
trum Sensing Algorithms”, IEEE Intl. Conf. on Commun. 2014, Jun 2014,
http://dx.doi.org/10.1109/ICC.2014.6883512.
7. [47] Pawan Dhakal, Daniel Riviello, Roberto Garello and Federico Penna,
“HybridApproachAnalysis ofEnergyDetectionandEigenvalueBased Spec-
trum Sensing Algorithms with Noise Power Estimation”, COCORA 2014,
The Fourth Intl. Conf. on Advances in Cognitive Radio, Feb. 2014, http:
//www.thinkmind.org/download.php?articleid=cocora_2014_1_40_80029.
Contents
ListofFigures xii
ListofTables xvii
Acronyms xviii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 StructureandContributionofThisThesis . . . . . . . . . . . . . . 11
2 OrderedStatisticsDecodingofLinearBlockCodes 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 ChannelCodingfor5G . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 OrderedStatisticsDecoding . . . . . . . . . . . . . . . . . . . . . 19
2.4 OverviewofOSDandConventionalReprocessing . . . . . . . . . . 21
2.5 ProposedNewStatisticofOrderedVectorComponents . . . . . . . 23
2.6 OSDErrorPerformance . . . . . . . . . . . . . . . . . . . . . . . 27
2.7 NumericalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.7.1 PmfofE . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
L
2.7.2 ErrorPerformance . . . . . . . . . . . . . . . . . . . . . . 29
3 FrameSynchronization 32
Contents ix
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.1.1 MachinetoMachineCommunication . . . . . . . . . . . . 32
3.2 IntroductiontoFrameSynchronizationProblem . . . . . . . . . . . 34
3.3 SystemModelandProblemdiscussion . . . . . . . . . . . . . . . . 35
3.4 LikelihoodRatioTest . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.1 EquivalencyofMasseyFormulationandChiani/Martinifor-
mulationofLRT . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 HardandSoftCorrelation . . . . . . . . . . . . . . . . . . . . . . . 39
3.6 TheMasseyCorrelationDetector . . . . . . . . . . . . . . . . . . . 40
3.7 AnalyticalcomputationofthedetectorperformancesunderH . . . 41
1
3.7.1 Miss-DetectionProbability . . . . . . . . . . . . . . . . . 45
3.8 AnalyticalcomputationofthedetectorperformancesunderH . . . 45
0
3.8.1 False-AlarmProbability . . . . . . . . . . . . . . . . . . . 50
3.8.2 False-alarmlowerbound . . . . . . . . . . . . . . . . . . . 50
3.9 ROCcurves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.10 TheExtendedMasseycorrelationdetector . . . . . . . . . . . . . . 54
3.10.1 Analytical computation of the Extended Massey detector
pdfunderH . . . . . . . . . . . . . . . . . . . . . . . . . 55
1
3.10.2 Analytical computation of the Extended Massey detector
pdfunderH . . . . . . . . . . . . . . . . . . . . . . . . . 57
0
3.10.3 ExtendedMasseydetectorperformances . . . . . . . . . . . 59
3.11 ComparisonagainstLRT,softandhardcorrelation . . . . . . . . . 60
3.11.1 Miss-detectionvs. SNRperformances . . . . . . . . . . . . 63
3.12 SomeAspectsforPracticalImplementation . . . . . . . . . . . . . 65
4 CognitiveRadio: SpectrumSensing 66
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.1.1 CognitiveRadioTechnology . . . . . . . . . . . . . . . . . 70