Federated Learning-Based Channel Estimation for RIS-Aided Communication Systems

Channel estimation is considered a fundamental task in achieving reconfigurable intelligent surface (RIS)-aided communication systems. However, the location distribution of users in a cell is nonuniform, which leads to worse channel estimation performance for a single neural network. In this letter, we propose two federated learning (FL)-based hierarchical networks to improve channel estimation performance. Specifically, a hierarchical neural network (HNet) is proposed to enhance the channel estimation accuracy, which can perform different channel feature extraction and mapping tasks for users in different regions. Furthermore, a hierarchical residual neural network (HReNet) is presented to reduce the communication overhead during model training. Simulation results reveal that the FL-based HNet and HReNet schemes yield better channel estimation performance when users are nonuniformly distributed.