Federated Learning-Assisted Predictive Beamforming for Extremely Large-Scale Antenna Array Systems With Rate-Splitting Multiple Access

Achieving perfect Channel State Information at the Transmitter (CSIT) is often infeasible in Extremely Large-scale Antenna Array (ELAA) systems due to user mobility and feedback/processing delay. This results in severe multi-user interference. Therefore, how to effectively and efficiently manage interference with partial/historical CSIT is one of the most important challenges for implementing ELAA. In this paper, we propose a Federated Learning (FL)-assisted predictive beamforming framework for ELAA systems to address this challenge. Specifically, we introduce Rate-Splitting Multiple Access (RSMA) to relax the sensitivity to imperfect CSIT while still benefiting from the spatial resolution. Moreover, a predictive beamforming protocol is designed to optimize the precoder design under the imperfections in the channel estimate quality originating from user mobility and latency. To calculate the beamformers, we first propose a lightweight patch-mixing approach to split the historical CSIT data samples into smaller manageable segments. Then, we propose an FL-based training method that enables parallel processing of these CSI segments, thereby accelerating the training process. Simulation results show the effectiveness and efficacy of the proposed FL-assisted predictive beamforming framework, which paves the way for real-world implementation of ELAA.