AI 中文总结
针对跳频TDD系统全频段信道获取难题，提出ST-DDA框架，利用多普勒域能量集中及窗口重叠特性，采用交替子空间等方法，在DDA域重建最新时隙，实验证明其相比基线在长探测间隔等情况下重建更优且单时隙运行时间相当。
AI 中文摘要
在跳频时分双工（TDD）系统中，精确的全频段信道获取具有挑战性，因为每个探测时隙仅观测有限的频率子带，传统单时隙恢复无法充分利用历史观测。我们提出ST-DDA，一种用于在多普勒-延迟-角度（DDA）域中进行最新时隙重建的在线稀疏子空间跟踪框架。首先表明在适度信道变化下多普勒域表示保持能量集中，支持加窗DDA域稀疏恢复。局部稳定性分析表明相邻窗口间的大量重叠使前一窗口估计能热启动每个窗口特定的恢复问题。为计算简便，ST-DDA采用交替子空间方法，结合利用局部角度和多普勒结构的位置编码卷积重加权。实验表明，重加权的ST-DDA比动态压缩感知基线实现更准确可靠的重建，尤其对于更长探测间隔和更大跳频周期，同时保持相当的单时隙运行时间。
英文摘要
Accurate full-band channel acquisition in frequency-hopping time-division duplex (TDD) systems is challenging because each sounding slot observes only a limited frequency subband, while conventional single-slot recovery cannot fully exploit historical observations. We propose ST-DDA, an online sparse-subspace tracking framework for latest-slot reconstruction in the Doppler--delay--angle (DDA) domain. We first show that the Doppler-domain representation remains energy-concentrated under moderate channel variation, thereby supporting windowed DDA-domain sparse recovery. A local stability analysis further shows that the substantial overlap between adjacent windows enables the preceding-window estimate to warm-start each window-specific recovery problem, allowing the optimization progress to be carried across slots under a fixed per-slot iteration budget. For computational tractability, ST-DDA employs the alternating subspace method, which restricts the regularized least-squares fidelity updates to support-induced subspaces, together with position-encoded convolutional reweighting that exploits local angular and Doppler structures. Experiments show that reweighted ST-DDA achieves more accurate and reliable reconstruction than dynamic compressed-sensing baselines, particularly for longer sounding intervals and larger frequency-hopping periods, while maintaining comparable per-slot runtime.
Comments13 pages, 6 figures, 4 tables; This work has been submitted to the IEEE for possible publication
