Machine Learning Empowered Large RIS-assisted Near-field Communications

Abstract

A large reconfigurable intelligent surface (LRIS) assisted wireless communication system is investigated in this paper. The increased aperture size and reconfigurable element number of LRIS bring new challenges, including limited incident beam coverage on LRIS, near-field signal propagation, and high beamforming complexity. Against these challenges, a two-step low-complexity beamforming approach is proposed, where a deep reinforcement learning (DRL) algorithm is invoked for determining the optimal beam direction, and a codebook based on the geometric channel state information is designed to map the direction to the beamforming matrixes. The proposed approach not only reduces the computational complexity, but also exploits the geometric channel of BS-LRIS to reduce the channel estimation complexity caused by LRIS. Simulation results indicate that the LRIS can further reduce power consumption compared to the small-size RIS. Meanwhile, the proposed joint codebook-DRL approach achieves a counterbalance compared to the sheer DRL algorithm with lower complexity.