DCO-OFDM系统中BPNN信道估计方法-豆柴文库

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DCO-OFDM系统中BPNN信道估计方法
Title:ChannelEstimationinDCO-OFDMSystemsusingBackpropagationNeuralNetwork
Abstract:
DirectCurrentOpticalOrthogonalFrequencyDivisionMultiplexing(DCO-OFDM)isapromisingmodulationschemeforvisiblelightcommunicationsystems.OneoftheessentialtasksinDCO-OFDMsystemsisaccuratechannelestimation,asitdirectlyaffectstheperformanceandreliabilityofthecommunicationsystem.ThispaperproposesanovelchannelestimationmethodusingaBackpropagationNeuralNetwork(BPNN)inDCO-OFDMsystems.TheBPNN-basedchannelestimationtechniqueaimstoovercomethelimitationsofconventionalmethodsandprovideimprovedestimationaccuracyandrobustnesstochannelvariations.Theadvantagesandlimitationsoftheproposedmethodarediscussed,andsimulationresultsarepresentedtovalidatetheeffectivenessoftheproposedapproach.
1.Introduction:
Visiblelightcommunication(VLC)hasgainedsignificantattentionasasupplementtoexistingwirelesscommunicationsystemsduetoitsunlicensedandenergy-efficientcharacteristics.DCO-OFDMisamodulationtechniqueusedinVLCsystems,whichutilizestheintensityvariationsofvisiblelighttotransmitdata.InDCO-OFDMsystems,accuratechannelestimationiscrucialformitigatingtheeffectsofmultipathpropagation,inter-symbolinterference,andotherimpairments.Conventionalchannelestimationmethods,suchasLeastSquares(LS)andMinimumMean-SquareError(MMSE),havelimitationsindealingwithnon-linearandtime-varyingchannels.Therefore,thereisaneedforamorerobustandaccuratechannelestimationtechnique.
2.BPNN-BasedChannelEstimationMethod:
TheproposedmethodutilizesaBackpropagationNeuralNetwork(BPNN)forchannelestimationinDCO-OFDMsystems.BPNNisawell-knownmachinelearningalgorithmusedforpatternrecognitionandpredictiontasks.TheBPNN-basedchannelestimationmethodconsistsofthefollowingsteps:
2.1TrainingPhase:
a.GenerationofTrainingDataset:Asignificantadvantageofaneuralnetwork-basedapproachistheabilitytolearnfromdata.ThetrainingdatasetisgeneratedbytransmittingknownpilotsymbolsthroughtheDCO-OFDMsysteminacontrolledenvironment.ThereceivedsymbolsareusedasinputtotheB