国际会议演讲稿Word格式文档下载.docx

国际会议演讲稿Word格式文档下载.docx

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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

Sowhydidweconductthisresearch?

Well,wewanttoexploitthepotentialbenefitsofdeeplearninginenhancingphysicallayersecurityincooperative（ 

/kəʊ'

ɒpərətɪv/ 

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（/'

hɪdn/）layer2andoutputlayer.Neuralnetworkcanlearnfeaturesfromrawdataautomaticallyandadjustparameters（/pəˈræ

mɪtə（r）z/）flexibly（ 

/'

fleksəbli/）suchasweightsandbiases.

Incomplex（ 

kɒmpleks/）conditions（scenarios（/sɪ'

nɑːrɪəʊ/）,）Neuralnetworkhaspromisingapplicationsinrelayselectionforseveralreasons.

First,thedeepnetworkhassuperior（/suːˈpɪərɪə/）learningabilitydespite（/dɪ'

spaɪt/）thecomplexchannelconditions.

Second,Neuralnetworkcanhandlelargedatasetsbecauseofdistributed（/dɪ'

strɪbjʊtɪd/）andparallel（/'

pæ

rəlel/）computing（/kəm'

pjuːtɪŋ/s,whichensurecomputation（/kɒmpjʊ'

teɪʃ（ə）n/）speedandprocessingcapacity（ 

/kə'

sɪtɪ/）.

Third,variouslibrariesorframeworks,suchasTensorFlow,Theano,andCaffegiveitwideapplications

Inthispaper,theproblemoftherelayselectionismodeledasamulti（/'

mʌltɪ/,ao）-classificationproblem.Weadoptsimpleneuralnetwork（NN）toselecttheoptimalrelaytoguaranteesperfectsecrecyperformanceofrelaycooperativecommunicationsystem.（enhancephysicallayersecurity）

P12

Beforetrainingtheclassificationmodel,weneedtomakesomepreparationfordeeplearningtoacquireatrainingsetandatestingset.

First,weneedtoproducerealfeaturevectorforeachexampleaccordingtochannelstateinformation;

becausethechannelstateinformationmatricesiscomposedofcomplexnumbersbutfeaturevectorsaregenerallycomposedofrealnumbers.Soweneedtochangecomplexnumbersintorealnumberswithabsolute（/'

æ

bsəluːt/）valueoperation.Moreover,inordertoimprovetheclassificationperformance（precision）,itisnecessarytonormalizethefeaturevectors.

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

Third,wecanmakelabelsforexamplesaccordingtoKPI.theindexoftherelaywhichobtainsthemaximum（ 

mæ

ksɪməm/）KPIisregardedastheclasslabeloftheexample.

P13

Classificationmodel

Thispicture（isabout）showsthewholeprocessofbuildingclassificationmodel.

Thewholeprocessofbuildingclassificationcanbedividedintotwophases,namelytrainingphaseandtestingphase.Inthefirstphase,weneedtochoosesuitablehyper（ 

haɪpə/）parameterstotrainneuralnetworkmodel.Inthesecondphase,wecanpredict（/prɪ'

dɪkt/）labelsofoptimalrelayaccordingtoinputdataandassessclassificationperformance.

P15

Nowletmemovetopartfour-----SimulationandResults（ 

/rɪˈzʌlts/）Analysis

Here,youcanseeafigurewhichshowstherelationshipbetweentheaveragetransmit（ 

/træ

nzˈmɪt/）powerofthesourceandtheachievablesecrecyratewithdifferentnumbersofrelays.

Inthisfigure,thebluelinerepresentstheconventionalrelayselectionschemeandtheredlinerepresentstheNN-basedscheme.

Inthisfigure,asthenumbersofrelayandtheaveragetransmitpowerofthesourceincreases,theachievablesecrecyrateincreasesaccordingly,.whichmeansincreasingthenumberofrelayscaneffectivelyimprovethesecretperformance.

Theredlinearealmost（ 

ɔːlməʊst/）closetotheblueline,【whchindicatesthatourproposedscheme（i.e.theNN-basedscheme）achievesalmostthesamesecrecyratesasthoseoftheconventionalschemeforallvaluesof

】whichvalidateseffectiveness（/ɪ'

fektɪvnɪs/）ofourproposedschemes.

P16

Thistableshowsthethenormalized（/ˈnɔrməˌlaɪzd/）meansquare（ 

/skweə/）error（NMSES）valuesofdirrerentrelaynodes.ThevalueofNMSEmeanstheperformancedifferencebetweentheconventionalschemeandourproposedscheme.ThevaluesofNMSEarebelow（/bɪ'

ləʊ/）negative（'

negətɪv/）20（

）,whichvalidateseffectivenessofourproposedschemeagain.

P17

Now,letmemovetothelastpart-----Conclusion

Okay,nowwearegoingtotakealookatthelastpart-Conclusion.

P18

Wehavegotthefollowingconclusions.

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

Second,Comparedwiththeconventionalrelayselectionscheme,ourproposedschemeachievesalmostthesamesecrecyperformance.

Andlast,Ourproposedschemehasanadvantage（/əd'

vɑːntɪdʒ/）ofrelativelysmallfeedbackoverhead,indicatingthatproposedschemecanbeappliedtotheconditions（scenarios）wherethefeedbackislimited.

（Iftheconventionalschemeneedsfeedbackof

complexnumbers,NN-basedschemewillonlyneedfeedbackof

realnumbers.Therefore,thefeedbackoverheadofourproposedschemeishalf（/hɑːf/ 

）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.

what’sthemeaningof“perfectsecrecyperformance”?

What’sthemeaningof“Comparedtotheconv