分布式机器学习在RIS辅助的无线信道估计中的应用OA北大核心CSTPCD

Wireless channel estimation is the key and prerequisite for deploying reconfigurable intelligent surface(RIS)-assisted communication system.However,the difficulty of channel estimation and the huge pilot overhead in the downlink transmission environment are major challenges for RIS-assisted communication.In order to solve above problems,a regional intersection switching scheme based on distributed machine learning(DML)technique is proposed.Firstly,a multi-user shared downlink channel estimation network is proposed,which is trained by DML technique in collaboration with users and base stations.Then,a hierarchical neural network structure is built to classify and extract features of user regional channels.Finally,the feature region model fusion is used to solve the problem of users at the intersection position of adjacent channels.The simulation results show that the DML model scheme based on regional intersection can reduce the channel training pilot overhead and maximize the accurate performance of channel estimation.