Radar-Aided Beam Selection in MIMO Communication Systems: A Federated Transfer Learning Approach

Abstract

By leveraging massive available data and hidden communication patterns, deep learning (DL) has enabled diverse applications in wireless network operations. In this paper, we consider radar-aided beam prediction in multi-input multi-output (MIMO) communication systems with federated transfer learning (FTL) to preserve users' location privacy. Specifically, we propose a novel structure, i.e., radar-aided federated transfer beam prediction (RaFT-BP), to achieve few samples-enabled distributed beam selection in internet of vehicles (IoV) scenarios. Simulation results show that the proposed RaFT-BP can achieve the 93.78% top-5 accuracy with 600 samples in the distributed node, enabling 11.9% to 33.2% beam selection accuracy improvement compared with baseline schemes.