Federated Learning-Aided Dual-Side Channel Estimation in Multi-User Massive MIMO Systems

In massive multiple-input multiple-output (MIMO) systems, attaining precise channel state information (CSI) tends to cause significant pilot overhead and prohibitive computational complexity. In light of this, data-driven approaches have been developed by training neural network models that recover channels from received signals. Since the conventional centralized learning (CL) would bring substantial communication overhead and privacy risks, federated learning (FL) has emerged as a promising alternative, allowing the base station (BS) to collect only model parameters from users, without the whole dataset. However, the previous work focus solely on either downlink or uplink channel estimation, neglecting the potential interplay between such two processes. In this letter, we design a consistent FL framework to estimate both the CSI at the receiver side (CSIR) and the CSI at the transmitter side (CSIT). Specifically, the beamformers at both end during preamble phases are delicately designed, which enables the parameters trained from the downlink models to be fully utilized for uplink channel estimation, thereby further reducing the communication overhead. Simulation results demonstrates that the proposed approach achieves better estimation performance compared to the existing analytical methods, and shows pleasurable robustness adapted to varying environments.