基于神经网络的无线信道建模与仿真.pdfVIP

基于神经网络的无线信道建模与仿真

Abstract

Introduction

1.NeuralNetworkBasics

1.1Perceptron

1.2FeedforwardNeuralNetworks

1.3Backpropagation

2.WirelessChannelModeling

2.1IntroductiontoWirelessChannels

2.2ChannelModels

2.2.1AdditiveWhiteGaussianNoiseChannel

2.2.2RayleighFadingChannel

2.2.3RicianFadingChannel

3.NeuralNetwork-basedChannelModeling

3.1MotivationforNeuralNetwork-basedChannelModeling

3.2DataCollectionforTraining

3.3NeuralNetworkArchitectureDesign

4.SimulationSetup

4.1SoftwareandHardwareRequirements

4.2DatasetGeneration

4.3NeuralNetworkTraining

5.PerformanceEvaluation

5.1QuantitativeMetrics

5.2SimulationResults

6.Discussion

6.1ComparisonwithTraditionalModels

6.2LimitationsandFutureDirections

7.Conclusion

Abstract

Wirelesschannelmodelingplaysavitalroleinthedesignand

performanceevaluationofwirelesscommunicationsystems.Thispaper

focusesontheuseofneuralnetworksforwirelesschannelmodeling

discussesdifferentwirelesschannelmodels,andpresentsadetailed

approachtodevelopingneuralnetwork-basedchannelmodels.A

simulationsetupisdescribed,andperformanceevaluationmetricsare

discussed.Thepaperconcludeswithadiscussionontheadvantages

Introduction

Withtheincreasingpopularityanddemandforwireless

communicationsystems,accuratemodelingofwirelesschannelshas

Gaussiannoisechannel,Rayleighfadingchannel,andRicianfading

channel,havebeenusedformanyyears.However,thesemodelsmay

channels.

1.NeuralNetworkBasics

Neuralnetworksarecomputationalmodelsinspiredbythestructure

andfunctionofbiologicalneuralnetworks.Thissectionprovidesa

briefoverviewofneuralnetworkfundamentals,includingthe

perceptron,feedforwardneuralnetworks,andbackpropagation.

1.1Perceptron

Aperceptronisthebasicbuildingblockofaneuralnetwork.It