ST-DDA: Dynamic Channel Estimation in the Doppler-Delay-Angle Domain via Sparse Subspace Tracking for TDD Systems
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.