Maximum A Posteriori Probability Channel Tracking with an Intelligent Transmitting Surface

TL;DR

This work develops a MAP-based channel-tracking framework for an ITS-enhanced base station, focusing on the dominant LoS UE–ITS link modeled by three parameters: $\beta_t$ (amplitude), $\omega_t$ (phase), and $\varphi_t$ (AoA). By updating priors from the previous coherence block, the method solves a MAP problem with closed-form updates for $\omega_t$ and $\beta_t$ and a one-dimensional search for $\varphi_t$, using only $L_t=2$ pilots per block and a myopic ITS-beam refinement strategy. Numerical results show the proposed MAP tracking achieves NMSE and spectral efficiency close to perfect CSI and outperforms ML under realistic prior-mobility mismatches, validating low-overhead, accurate channel tracking for ITS-assisted mmWave networks. The approach also generalizes to RIS-based reflections, demonstrating broad applicability to parametric, low-pilot-channel-tracking in reconfigurable surface systems.

Abstract

This paper considers an intelligent transmitting surface (ITS) integrated into a base station and develops a low-overhead maximum a posteriori (MAP) probability channel tracking method for the dominant line-of-sight link between the ITS and the user equipment. We cast the per-block channel as a three-parameter model consisting of the channel amplitude, channel phase, and angle-of-arrival at the ITS. We exploit temporal correlation by updating the priors using the estimates from the previous block. Using only two pilots per coherence block alongside a targeted beam alignment strategy, the proposed method achieves precise channel tracking and attains spectral efficiency close to that achievable under perfect channel knowledge.

Figures (3)

• Figure 1: NMSE versus SNR.
• Figure 2: AoA tracking.
• Figure 3: SE versus SNR.