国际会议演讲稿修订版.docx

国际会议演讲稿修订版.docx

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国际会议演讲稿修订版

HUAsystemofficeroom【HUA16H-TTMS2A-HUAS8Q8-HUAH1688】

国际会议演讲稿

自我介绍

Thankyou,Mr./Ms.Chair./professor

Mynameissangqian.Iamveryhonoredtobeheretodooralpresentation.

IamaMasterstudentfromHohaiUniversityandIamcurrentlydoingsomeresearchonphysicallayersecurity.

Today,Iwouldliketosharewithyousomeofmyresearchonrelayselectionincooperativecommunication.（external/ekstrnl;kstrnl/）

内容安排：

Mypresentationincludesthesefiveparts.

First,somebackgroundinformationaboutthisresearch;

Second,systemmodelwehavedone;

Third,NN-basedrelayselectionschemewehaveproposed

Forth,Simulationandresultsanalysis

Andlast,someconclusionswehavegot

P4

Partone,introduction

Firstly,Iwouldliketogiveyouabitofbackground.

Differingfromthetraditionalcryptographictechniquesbasedonsecretkeys,wecanmakeuseofwirelesschannelcharacteristicstoenhancephysicallayersecurity.

Cooperativecommunicationhasbeenwidelyrecognizedasaneffectivewaytocombatwirelessfadingandprovidediversitygainwhichisoneoftheresearchhotspots.

Machinelearningasanemergingtechnologyhasbeenwidelyappliedinimageprocessing,cancerprediction,stockanalysisandotherfields.Sowhynottryitinwirelesscommunication?

P5:

Next,Iwanttotalkalittlebitaboutpresentstudy

Recentstudiesondeeplearningforwirelesscommunicationsystemshaveproposedalternativeapproachestoenhancecertainpartsoftheconventionalcommunicationsystemsuchasmodulationrecognition、channelencodinganddecoding、channelestimationanddetectionandanautoencoderwhichcanreplacethetotalsystemwithanovelarchitecture

【modulationrecognition:

AnNNarchitectureformodulationrecognitionthatconsistsofa4-layerNNandtwotwo-layerNNs。

channelencodinganddecoding：

AplainDNNarchitectureforchanneldecodingtodecodekbits

messagesfromNbitsnoisycodewords。

channelestimationanddetection：

Adense-Netforsymbol-to-symboldetectioncanadoptlongshort-termmemory（LSTM）todetectanestimatedsymbol.

Autoencoder：

theautoencodercanrepresenttheentirecommunicationsystemandjointlyoptimizethetransmitterandreceiveroveranAWGNchannel.

】

P6

SowhydidweconductthisresearchWell,wewanttoexploitthepotentialbenefitsofdeeplearninginenhancingphysicallayersecurityincooperative（/k'prtv/wirelesscommunicationandreducethefeedbackoverheadinlimitedspectrumresoucebyourourproposedscheme.

P8

Nowletmemoveontoparttwo-systemmodel

Here,youcanseeafigurewhichisasystemmodel.

Thisisthesource;thesearetherelaynodesandthisisthedestination,thisistheeavesdropper

Thewholeprocessofcooperativewirelesscommunicationcanbedividedintotwophases.

Inthefirstphase,thesourcebroadcaststhesignaltotheoptimalrelaywhichguaranteesperfectsecurity.AsshowninFig1,

representsafadingcoefficientofthechannelfromthesourcetotherelaynode（

.）

Inthesecondphase,theoptimalrelayforwardsascaledversionofitsreceivedsignaltothedestinationinthepresenceoftheeavesdropper,wheretheoptimalrelayisconsideredtoadoptamplify-and-forward（AF）relayscheme.

Inthisfigure,

representsafadingcoefficientofthechannelfromtherelay

tothedestination

representsafadingcoefficientofthechannelfromtherelay

totheeavesdropper.

P9:

Hereyoucanseesomefollowingexpressions.Iamnotgoingtowasteourprecioustimeonthelengthyderivation.Iwouldliketoinviteyoutodirectlytakealookattheequationinitsfinalform.

Thisistheoptimalindexoftheselectedrelaywiththeconventionalrelayselectionscheme.Amaongthisexpression

representstheachievablesecrecyrateofsystemmodelwhenthe

relayisselected.

P11

Nowletmemovetopartthree-----NN-basedRelaySelection

Hereyoucanseeafigurewhichshowsconventional3-layerneuralnetwork.Itconsistsofinputlayer,hiddenlayer1,hidden（/'h?

dn/）layer2andoutputlayer.Neuralnetworkcanlearnfeaturesfromrawdataautomaticallyandadjustparameters（/prmt（r）z/）flexibly（/'fleksbli/）suchasweightsandbiases.

Incomplex（/'kmpleks/）conditions（scenarios（/s'nɑ?

r/）,）Neuralnetworkhaspromisingapplicationsinrelayselectionforseveralreasons.

First,thedeepnetworkhassuperior（/supr/）learningabilitydespite（/d'spat/）thecomplexchannelconditions.

Second,Neuralnetworkcanhandlelargedatasetsbecauseofdistributed（/d'strbjtd/）andparallel（/'prlel/）computing（/km'pjut/s,whichensurecomputation（/kmpj'te（）n/）speedandprocessingcapacity（/k'pst/）.

Third,variouslibrariesorframeworks,suchasTensorFlow,Theano,andCaffegiveitwideapplications

Inthispaper,theproblemoftherelayselectionismodeledasamulti（/'mlt/,ao）-classificationproblem.Weadoptsimpleneuralnetwork（NN）toselecttheoptimalrelaytoguaranteesperfectsecrecyperformanceofrelaycooperativecommunicationsystem.（enhancephysicallayersecurity）

P12

Beforetrainingtheclassificationmodel,weneedtomakesomepreparationfordeeplearningtoacquireatrainingsetandatestingset.

First,weneedtoproducerealfeaturevectorforeachexampleaccordingtochannelstateinformation;becausethechannelstateinformationmatricesiscomposedofcomplexnumbersbutfeaturevectorsaregenerallycomposedofrealnumbers.Soweneedtochangecomplexnumbersintorealnumberswithabsolute（/'bslut/）valueoperation.Moreover,inordertoimprovetheclassificationperformance（precision）,itisnecessarytonormalizethefeaturevectors.

Second,weneedtodesignkeyperformanceindicator（KPI）.Inordertoeffectivelypreventtheeavesdropperfrominterceptinginformation,wechooseachievablesecrecyrateastheKPIofsystem.ThisKPIindicates（represents/shows）thedifferenceoftheachievableratefromthesourcetothedestinationandtheachievableratefromthesourcetotheeavesdropper.

Third,wecanmakelabelsforexamplesaccordingtoKPI.theindexoftherelaywhichobtainsthemaximum（/'mksmm/）KPIisregardedastheclasslabeloftheexample.

P13

Classificationmodel

Thispicture（isabout）showsthewholeprocessofbuildingclassificationmodel.

Thewholeprocessofbuildingclassificationcanbedividedintotwophases,namelytrainingphaseandtestingphase.Inthefirstphase,weneedtochoosesuitablehyper（/'hap/）parameterstotrainneuralnetworkmodel.Inthesecondphase,wecanpredict（/pr'dkt/）labelsofoptimalrelayaccordingtoinputdataandassessclassificationperformance.

P15

Nowletmemovetopartfour-----SimulationandResults（/rzlts/）Analysis

Here,youcanseeafigurewhichshowstherelationshipbetweentheaveragetransmit（/trnzmt/）powerofthesourceandtheachievablesecrecyratewithdifferentnumbersofrelays.

Inthisfigure,thebluelinerepresentstheconventionalrelayselectionschemeandtheredlinerepresentstheNN-basedscheme.

Inthisfigure,asthenumbersofrelayandtheaveragetransmitpowerofthesourceincreases,theachievablesecrecyrateincreasesaccordingly,.whichmeansincreasingthenumberofrelayscaneffectivelyimprovethesecretperformance.

Theredlinearealmost（/'lmst/）closetotheblueline,【whchindicatesthatourproposedscheme（i.e.theNN-basedscheme）achievesalmostthesamesecrecyratesasthoseoftheconventionalschemeforallvaluesof

】whichvalidateseffectiveness（/'fektvns/）ofourproposedschemes.

P16

Thistableshowsthethenormalized（/nrmlazd/）meansquare（/skwe/）error（NMSES）valuesofdirrerentrelaynodes.ThevalueofNMSEmeanstheperformancedifferencebetweentheconventionalschemeandourproposedscheme.ThevaluesofNMSEarebelow（/b'l/）negative（'negtv/）20（

）,whichvalidateseffectivenessofourproposedschemeagain.

P17

Now,letmemovetothelastpart-----Conclusion

Okay,nowwearegoingtotakealookatthelastpart-Conclusion.

P18

Wehavegotthefollowingconclusions.

First,Incomplex（conditions）scenarios,Neuralnetworkhaspromisingapplicationsinrelayselectionforsuperiorlearningability,computationspeedandprocessingcapacity.

Second,Comparedwiththeconventionalrelayselectionscheme,ourproposedschemeachievesalmostthesamesecrecyperformance.

Andlast,Ourproposedschemehasanadvantage（/d'vɑntd/）ofrelativelysmallfeedbackoverhead,indicatingthatproposedschemecanbeappliedtotheconditions（scenarios）wherethefeedbackislimited.

（Iftheconventionalschemeneedsfeedbackof

complexnumbers,NN-basedschemewillonlyneedfeedbackof

realnumbers.Therefore,thefeedbackoverheadofourproposedschemeishalf（/hɑ?

f/）ofthatoftheconventionalscheme,）

Q&A

1、计算复杂度

Computationalcomplexity

Thebiggestdrawbackisthehighlyselectioncomplexitieswithasmallnumberofrelaynodes.

Ifnumberofrelaynodeisbig,itwillhaveaadvantage.Thisneedourfurtherresearch.

Q:

TheexperimentshowsthatsecrecyrateisalmostthesameastraditionalmethodandwhatisthepromotionofusingNNtorelayselection.（whatismeaningofintroducingNNtorelayselection）

A:

Thatourproposedscheme（i.e.theNN-basedscheme）achievesalmostthesameachievablesecrecyrateasthatoftheconventionalschemeindicatesthatourproposedschemeiseffectiveanditcanselectoptimalrelaynodewhichobtainsmaximumachievablesecrecyrate.

Onereason（thefirstreason）isthatAdoptingNNforrelayselectionisanovelidea.

Anotherreasonisthatthespectrumresourceisrelativelimitedandourproposedschemehassmallfeedbackoverhead.

Q:

what’sthemeaningof“perfectsecrecyperformance”What’sthemeaningof“Comparedtotheconventionalrelayselectionscheme”

A:

“perfe