Physics-Guided Neural Airy Beamforming for Near-Field Blockage Mitigation

Abstract

High-frequency communication systems heavily rely on line-of-sight(LoS) paths, so blockage of the LoS path can cause severe performance loss. Near-field Airy beams with curved trajectories can steer energy around obstacles, offering a promising solution for blockage mitigation. However, existing methods for selecting a near-optimal Airy beam trajectory either rely on high-overhead beam training, or employ data-driven learning without a clear, physically interpretable rule. To address this problem, we propose a physics-guided neural Airy beamforming framework that selects a near-optimal trajectory in one shot with clear physical interpretability. Specifically, we first formulate a single-edge representation of the blocker model in 3GPP TR 38.901 and reveal the trajectory--edge coupling mechanism. This analysis yields a trajectory-selection optimality condition that defines the candidate trajectories. Although these trajectories generally cannot be expressed in closed form, we show that they form a continuous structure. This continuous structure is then exploited to construct a compact physics-defined region that captures near-optimal trajectories. Guided by this region, a lightweight neural predictor is finally designed to directly select a near-optimal trajectory without beam training. Simulations show that the compact physics-defined region effectively captures near-optimal trajectories, while occupying only about 6% of the candidate-space area on average. The proposed framework retains 99.7% of the reference rate obtained through numerical optimization, and nearly matches the rate of the data-driven method despite using approximately 112\times fewer neural-network parameters.