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1、_●■東北大學碩士學位論文AbstractResearchandSimulationofWirelessChannelModelBasedonNeuralNetworkAbstractInthemobilecommunicationsystem，thereceivesignalshavebeeninevitablyaffectedbymultipathfadingandtheeffectofshadowingwhenmobileusersaremoving．Whendesigningthecommuncations
2、ystem，thepropagationcharacteristicsofwirelesschannelarethemostimportantfactor,whichdirectlyinfluencethequalityofcommunicationserviceandthestablityofthecommunicationsystem．Andtherefore，theresearchesofthecharacteristicsofradiopropagationandwirelessmobilechannelm
3、odelsarenecessarytodevelopanddesignanexcellentwirelesscommunicationsystem．Wirelesschannelmodelingandsimulationaremainlystudiedinthisthesis．Themaincontentofthethesisisdevidedintotwoparts：theexistingflatfadingchannelmodelingandsimulationmethodsarestudiedinthefir
4、stpart，andtheirownadvantagesanddisadvantagesbysimulatoinsarealsoanalyzed．Andoneofmodelingmethodsisappliedtothefrequencyselectivefadingchannelsimulation,andthesimulationresultsaregiven．Thewirelesschannelcanberegardedasanonlineartransimissionsystem，andtheneuraln
5、etworkhasfitlyfavorableperformanceinsimulatingnonlinearsystem．Soamodelingmethodofwirelesschannelbasedonneuralnetworkisproposedinthesecondpartofthispaper．Inthispart,thenewchannelmodelisstudiedanddevelopedfromtheshallowtothedeep：anAdditiveWhiteGaussianNoise(AWGN
6、)channelmodelbasedonBPneuralnetworkiscreatedfirstly,andissimulatedbybasicBPalgorithmandimprovedBPalgorithmrespectively．ThesimulationresultsshowthatBPneuralnetworkhasacertainapproachcapacity,buttheconvergencerateofBPalgorithmistooslow,withexistenceoflocalminimu
7、mvalueinobjectivefunction；theimprovedBPalgorithmenhancestheconvergencerate，butthereisstillacertainerrorbetweenactualoutputanddesiredoutput，withinaptitudeinselectinghiddenlayerneuronsnumbers．AsRadialBasisFunction(RBF)neuralnetworkcanapproachanynonlinearfunction
8、witharbitraryprecisionandhasafastlearningspeed，nolocalminimumvalue，anAWGNchannelmodelbasedonRBFneuralnetworkisproposed．Andthenewmodelingmethodisextendedtotheflatfadingchannelandfre
