摘要
Reconfigurable intelligent surface(RIS)has been proposed as a potential solution to improve the coverage and spectrum efficiency for future wireless communication.However,the privacy of users’data is often ignored in previous works,such as the user’s location information during channel estimation.In this paper,we propose a privacy-preserving design paradigm combining federated learning(FL)with RIS in the mmWave communication system.Based on FL,the local models are trained and encrypted using the private data managed on each local device.Following this,a global model is generated by aggregating them at the central server.The optimal model is trained for establishing the mapping function between channel state information(CSI)and RIS’configuration matrix in order to maximize the achievable rate of the received signal.Simulation results demonstrate that the proposed scheme can effectively approach to the theoretical value generated by centralized machine learning(ML),while protecting user’privacy.
基金
supported in part by the National Natural Science Foundation of China under Grant 61901378,61941119,61901379
in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2019JQ-253
in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University under Grant 2020D04
in part by China Postdoctoral Science Foundation under Grant BX20190287
in part by the Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation
in part by the Shanghai Aerospace Science and Technology Innovation Fund(No.SAST2018045)
in part by the China Fundamental Research Fund for the Central Universities(No.3102018QD096)
in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University(No.CX2020152).
