Spatial Information Aided Joint UL/DL Channel Tracking for Massive MIMO System in HST Environment

High-speed train (HST) communication has emerged as a prominent area of research, given its critical role in meeting the demanding requirements of high mobility scenarios like auto-driving and mission-critical train control signaling. The provision of these services relies heavily on the accurate acquisition of channel state information (CSI). However, traditional methods of channel estimation are inadequate for massive multiple-input multiple-output (MIMO) HST channels due to their extremely short channel coherence time, typically in the microsecond range. In this paper, we propose a dynamic grid-based joint uplink/downlink (UL/DL) channel tracking scheme in the angular domain, specifically designed to address fast time-varying channels in HST communication. The spatial reciprocity between UL and DL channels in frequency division duplex (FDD) systems and fuzzy spatial information are exploited to ensure the consistency of UL/DL supports and improve the tracking performance of the line-of-sight (LOS) channel, respectively. Then, we formulate a sparse Bayesian learning (SBL) problem and adopt a block majorization-minimization (MM) algorithm, where a significant gradient-based fast search (SGFS) algorithm is devised to solve the non-convex maximum a posteriori (MAP) problem for the angle of arrivals/departures (AoAs/AoDs) estimation. Finally, simulations verify the superiority of joint UL/DL channel tracking and show that the proposed scheme converges quickly with more accurate tracking results.