Study of Iterative Dynamic Channel Tracking for Multiple RIS-Assisted MIMO Systems
Roberto C. G. Porto, Rodrigo C. de Lamare
TL;DR
This work tackles the challenge of scalable channel estimation for multi-RIS MIMO systems under time-varying channels. It introduces an iterative detection, decoding, and channel estimation framework that uses LDPC-encoded pilots and encoded data to enhance channel estimates while dramatically reducing pilot overhead, leveraging temporal coherence via a Gauss–Markov channel model. The method separates direct and cascaded RIS channels, then refines them iteratively using EP in an IDD loop, aided by semi-orthogonal pilot-symbol designs. Temporal reuse of prior estimates accelerates convergence and reduces training needs, with simulations showing notable NMSE and spectral-efficiency gains in both LOS and NLOS scenarios, particularly when pilot budgets are tight. The approach is generalizable to advanced RIS architectures and supports LOS/NLOS channel tracking with reduced pilot overhead in dynamic environments.
Abstract
The use of multiple Reconfigurable Intelligent Sur- faces (RIS) has gained attention in 6G networks to enhance coverage. However, the feasibility of deploying multiple RIS relies on efficient channel estimation and reduced pilot overhead. To address these challenges, this work proposes an iterative channel estimation scheme that exploits low-density parity-check (LDPC) codes, channel coherence time, and iterative processing to improve estimation accuracy while minimizing pilot length. Encoded pilots are used to strengthen the iterative processing, leveraging both pilot and parity bits, while previous estimates are incorporated to further reduce overhead. Simulations consider a sub-6 GHz scenario with non-sparse channels and multiple RIS under both LOS and NLOS conditions. The results show that the proposed method outperforms existing approaches, achieving significant gains with substantially lower pilot overhead.