基于CSI的对称加密端到端通信系统

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中图分类号：TN918.91 文献标识码：A DOI：10.7535/hbkd.2025yx04006

CSI-based symmetric encryption end-to-end communication system

AN Yongli1²，LI Zongrui¹，BAI Haifei¹，MI Xueyu²，3 （1.CollgeofArticial Inteligence，North ChinaUniversityof ScienceandTechnology，Tangshan，Hebei O630oo，China; 2.Tangshan Key Laboratory of Air and Ground Smart Transportation，Tangshan，Hebei O63Ooo，China; 3.College of Emergency Management and Safety Enginering，North China Universityof Science and Technology， Tangshan，Hebei O630oo，China）

Abstract：Toaddress the isueof information leakage caused bykeytheftduring transmision，asymmetric encryptio endto-endcommunicationsystem basedonchannel stateinformation（CSI）was proposed.The proposedsystem employed convolutional neural networks toconstructthetransmiter，receiver，andkeygenerator，optimizing theencodinganddecoding process in anend-to-end manner.Atthesame time，itleveragedthereciprocity，random time-variability，andspatial uniquenessofwirelesschannels to measure the CSIandgenerate keys fromlegitimateusers，encryptingtheoriginal information.ThesimulationresultsdemonstratethatunderRayleighfading，Ricianfading，andfrequency-selectivemultipath fadingchanels，thebiterorrates（BER）of theproposedsystemislowerthanthatofbaselinemodelssuchasthesymmetric encryptionsystem basedondepconvolutional generative adversarial networks within thetested signal-to-noiseratio（SNR） range.In high-SNR scenarios，theimprovementof BERcanreach18dB.Aditionally，infouratack scenarios，suchas brute force andkey leakageattacks，theBERof eavesdropperisapproximatelyO.5，indicatinganinabilitytodecrypt the information.The proposed systemeliminates thenedforkeydistribution，reducing theBER whileenhancing eavesdropping resistance，thus providing a novel approach for secure communication.

Keywords：wireless communication technology；end-to-end communication；physical layer security；AutoEncoder；symmetric encryption

近年来，深度学习（deep learning，DL）发展迅速，许多学者将其引人到无线通信领域中，从物理层的角度对通信系统进行优化[1]。（剩余16288字）