发表机构
University of California, Los Angeles（加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对双色散信道下OTFS符号检测难题，本文提出融合物理信息学习初始器与迭代优化网络的两阶段迭代检测器，仿真显示其性能优于传统及现有学习型检测器。
AI 中文摘要
正交时频空间（OTFS）调制因能在时间和频率维度提供分集，已成为高移动性无线通信系统的极具潜力的候选方案。然而，在双色散信道下，可靠的OTFS检测仍存在挑战，其中时延和多普勒色散会在发射符号间引入结构化干扰，使符号恢复复杂化。为应对这些挑战，本文提出一种两阶段迭代OTFS检测器，它融合了物理信息驱动的学习初始器与迭代优化网络，可在双色散信道中逐步提升符号估计的精度。初始器结合已知的时延-多普勒输入输出关系，生成鲁棒的第一阶段估计值；优化阶段则在时延-多普勒域中迭代抑制残留的符号干扰。仿真结果表明，所提检测器在各类信道条件下，相较于传统检测器及现有基于学习的检测器，均实现了稳定的性能增益。这些结果凸显了将已知信道结构融入检测过程、采用迭代优化以实现更优且鲁棒的OTFS检测的有效性。
英文摘要
Orthogonal time frequency space (OTFS) modulation has emerged as a promising candidate for high-mobility wireless communication systems due to the diversity it offers across both time and frequency. Reliable OTFS detection, however, remains challenging under doubly-dispersive channels, where delay and Doppler dispersion induce structured interference between transmitted symbols and complicate symbol recovery. To address these challenges, we propose a two-stage iterative OTFS detector that integrates a physics-informed learned initializer with an iterative refinement network, enabling progressively more accurate symbol estimates in doubly-dispersive channels. The initializer incorporates the known delay-Doppler input-output relationship to produce a robust first-stage estimate, while the refinement stage iteratively suppresses residual symbol interference in the delay-Doppler domain. Simulation results demonstrate that the proposed detector achieves consistent performance gains over conventional and existing learning-based detectors across a variety of channel conditions. These results highlight the effectiveness of incorporating known channel structure into the detection process and using iterative refinement for improved and robust OTFS detection.
