AI 中文总结
该研究针对近场通信视距阻塞问题，提出物理引导的神经艾里波束成形框架，无需高开销波束训练即可高效选择近最优轨迹，性能接近数据驱动方法且参数量大幅减少。
AI 中文摘要
高频通信系统严重依赖视距（LoS）路径，因此视距路径被阻塞会导致严重的性能损失。具有弯曲轨迹的近场艾里波束能够将能量引导绕过障碍物，为阻塞缓解提供了一种有前景的解决方案。然而，现有的近最优艾里波束轨迹选择方法要么依赖高开销的波束训练，要么采用数据驱动学习却缺乏清晰的物理解释规则。为解决该问题，我们提出了一种物理引导的神经艾里波束成形框架，该框架可一次性选择近最优轨迹且具有清晰的物理解释性。具体而言，我们首先在3GPP TR 38.91标准中建立了阻塞物模型的单边缘表示，并揭示了轨迹-边缘耦合机制。该分析得出了定义候选轨迹的轨迹选择最优条件。尽管这些轨迹通常无法用闭式表达，但我们证明它们构成了连续结构。随后，利用该连续结构构建了一个紧凑的物理定义区域，该区域可捕获近最优轨迹。在该区域的引导下，最终设计了一个轻量级神经预测器，无需波束训练即可直接选择近最优轨迹。仿真结果表明，该紧凑的物理定义区域平均仅占用候选空间面积的约6%，却能有效捕获近最优轨迹；所提框架保留了数值优化得到的参考速率的99.7%，且尽管神经网络参数数量减少了约112倍，其速率仍几乎与数据驱动方法相当。
英文摘要
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.
