用深度学习设计可调光束，一次发射即可精准定位用户位置。
SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming
- 将相位移和时延系数设为可训练参数，端到端优化光束与定位性能。
- 单次下行传输后，通过最大接收功率和子载波索引实现二维定位，误差更低。
- 相比传统方法，通信开销降低一个数量级，适合高精度低延迟场景。
相位-时延阵列（Phase-time arrays）通过集成相位移器（PS）和真时延（TTD），成为宽带感知与定位中生成频率依赖的彩虹光束的低成本架构。本文提出一种基于深度学习的端到端方案，同时设计彩虹光束并估计用户位置。将PS与TTD系数视为可训练变量，使网络能合成面向任务的光束以最大化定位精度。一个轻量级全连接模块从单次下行传输后的最大量化接收功率及其对应子载波索引中恢复用户的角-距坐标。相比现有解析与学习方法，该方法将开销降低一个数量级，并持续实现更低的二维定位误差。
原文摘要 · Abstract (English)
Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating frequency-dependent rainbow beams in wideband sensing and localization. This paper proposes an end-to-end deep learning-based scheme that simultaneously designs the rainbow beams and estimates user positions. Treating the PS and TTD coefficients as trainable variables allows the network to synthesize task-oriented beams that maximize localization accuracy. A lightweight fully connected module then recovers the user's angle-range coordinates from its feedback of the maximum quantized received power and its corresponding subcarrier index after a single downlink transmission. Compared with existing analytical and learning-based schemes, the proposed method reduces overhead by an order of magnitude and delivers consistently lower two-dimensional positioning error.
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