AI-Empowered Beam Tracking for Near-Field Communications

A near-field multi-input multi-output (MIMO) multi-user downlink system is investigated. To achieve efficient beam tracking in near field, the trajectories of mobile users (MUs) are first predicted, and then successive hybrid beamfocusing with data stream allocation is performed to maximize the throughput. Specifically, a digit-aware location prediction framework based on Transformer is proposed to predict the subsequent locations of MUs. Through attending to the individual decimal digits of the MUs' locations, the prediction error can be reduced from the digit perspective. We then propose a dual-tiered proximal policy optimization algorithm to learn the adaptive hybrid beam-focusing and data stream allocation according to the predicted MUs' movement. The policy of agent is hierarchically designed for effective dimensionality reduction of the large-scale action space. The numerical results demonstrate that 1) Our proposed algorithms outperform the baselines in terms of throughput with high predictive accuracy and beamfocusing gain; 2) The proposed beam tracking scheme can achieve a similar throughput to the perfect CSI scheme, while the performance gap of the non-tracking scheme is 53.2%; 3) Compared to the fixed data stream allocation, the proposed adaptive data stream allocation benefits a performance gain which escalates with an increasing number of total data streams.