Enhancing Channel Estimation in RIS-aided Systems via Observation Matrix Design

2025-10-18

AI Summary

To address low channel estimation accuracy and excessive pilot overhead in reconfigurable intelligent surface (RIS)-assisted systems, this paper proposes a joint channel estimation framework grounded in information theory and Riemannian optimization. The method designs a high-efficiency observation matrix by maximizing mutual information, introduces an Alternating Riemannian Manifold Optimization (ARMO) algorithm to jointly optimize the receiver combining weights and the RIS phase-shift matrix, and develops an adaptive kernel training strategy—requiring no additional pilots—to dynamically update the channel covariance matrix. Simulation results demonstrate that the proposed approach significantly outperforms state-of-the-art schemes in terms of normalized mean square error (NMSE) and channel prediction error. It achieves a favorable trade-off among estimation accuracy, robustness, and low pilot overhead, thereby establishing an efficient and reliable paradigm for practical RIS channel acquisition.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for enhancing wireless communications through dense antenna arrays. Accurate channel estimation is critical to unlocking their full performance potential. To enhance RIS channel estimators, this paper proposes a novel observation matrix design scheme. Bayesian optimization framework is adopted to generate observation matrices that maximize the mutual information between received pilot signals and RIS channels. To solve the formulated problem efficiently, we develop an alternating Riemannian manifold optimization (ARMO) algorithm to alternately update the receiver combiners and RIS phase-shift matrices. An adaptive kernel training strategy is further introduced to iteratively refine the channel covariance matrix without requiring additional pilot resources. Simulation results demonstrate that the proposed ARMO-enhanced estimator achieves substantial gains in estimation accuracy over state-of-the-art methods.

Problem

• Enhancing RIS channel estimation accuracy via optimized observation matrix design
• Developing alternating Riemannian optimization for efficient channel estimation
• Improving channel covariance estimation through adaptive kernel training strategy

Innovation

• Bayesian optimization designs observation matrices for mutual information
• Alternating Riemannian manifold optimization updates receiver and RIS
• Adaptive kernel training refines channel covariance without extra pilots