Near-Field Wideband Hybrid Beamforming With Deep Reinforcement Learning

A deep reinforcement learning (DRL) method is proposed for hybrid beamforming in near-field wideband communication systems, aiming to alleviate the harmful near-field beam splitting issue caused by the spatial-wideband effect. Compared to the far-field beam splitting issue that occurs only in the angle domain, the near-field beam splitting issue extends to the distance domain and is thus more challenging to address. A hybrid beamforming architecture with true-time delayers (TTDs) is exploited to address the near-field beam splitting issue and facilitate the maximization of spectral efficiency (SE) across large bandwidth for downlink multi-user communications. However, the conventional iterative hybrid beamforming optimization methods, such as the weighted minimum mean-square method, often exhibit high computational complexity and are thus difficult to adapt the dynamic channel in a real-time manner. Hence, a DRL-based algorithm is propose to select fully-digital codewords from the designed near-field wideband beamforming codebook. To further approximate the selected codewords in the considered hybrid-beamforming architecture, a three-stage hybrid beamforming approximation (HBA) algorithm is proposed to minimize the fully-digital approximation error by jointly optimizing the TTD-based analog beamformers and baseband digital beamformers. Numerical results show that: (1) the proposed DRL-HBA solution for hybrid beamforming significantly increases the SE; (2) the hybrid beamforming architecture with TTDs can eliminate the near-field beam splitting effect and achieve beam focusing across wide band.