Channel Estimation in RIS-Aided Heterogeneous Wireless Networks via Federated Learning

Abstract

Downlink channel estimation in reconfigurable intelligent surface (RIS)-assisted communication systems employing federated learning (FL) is challenging due to communication/ computational overhead, users heterogeneity, and vulnerability to malicious users. This letter proposes a novel methodology integrating principal component analysis (PCA)-based clustering with FL, tailored for heterogeneous users. The approach effectively identifies regions and users within the cell while minimizing communication/computational overhead associated with clustering, resulting in accurate, resource-efficient, and secure channel estimation. Simulation results demonstrate that the proposed FL strategy achieves estimation performance comparable to the conventional methods while significantly reducing the communication overhead, enhancing the system security, and handling heterogeneous users.