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.

翻译：相位时间阵列（整合了移相器和真实时间延迟器）已成为在宽带感知与定位中生成频率相关彩虹波束的一种经济高效架构。本文提出了一种端到端的基于深度学习的方案，可同时设计彩虹波束并估计用户位置。通过将移相器和真实时间延迟器系数视为可训练变量，网络能够合成以任务为导向的波束，从而最大化定位精度。随后，一个轻量级全连接模块根据用户在单次下行链路传输后反馈的最大量化接收功率及其对应的子载波索引，恢复出用户的角度-距离坐标。与现有的解析方法和基于学习的方案相比，所提方法将开销降低了一个数量级，并持续实现了更低的二维定位误差。