Receiving RISs: Enabling Channel Estimation and Autonomous Configuration

2025-06-12

AI Summary

Accurate channel estimation and autonomous reflection configuration remain challenging for semi-passive reconfigurable intelligent surfaces (RIS) in MIMO systems. Method: This paper proposes a hardware architecture integrating RF receive chains and baseband processing units at the RIS. It introduces a non-orthogonal pilot reception mechanism at the RIS side, combined with absorptive phase tuning to enable spatial random sampling. Leveraging beam-domain sparsity and low-rank structure of the channel, we formulate an end-to-end joint optimization framework for channel estimation and reflection coefficient design based on the alternating direction method of multipliers (ADMM). Contribution/Results: The framework supports dynamic optimization under practical phase quantization constraints. Experimental results demonstrate significant improvements in estimation accuracy and capacity逼近 performance at FR3 and higher frequency bands, consistently outperforming state-of-the-art baselines across diverse hardware configurations.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This chapter focuses on a hardware architecture for semi-passive Reconfigurable Intelligent Surfaces (RISs) and investigates its consideration for boosting the performance of Multiple-Input Multiple-Output (MIMO) communication systems. The architecture incorporates a single or multiple radio-frequency chains to receive pilot signals via tunable absorption phase profiles realized by the metasurface front end, as well as a controller encompassing a baseband processing unit to carry out channel estimation, and consequently, the optimization of the RIS reflection coefficients. A novel channel estimation protocol, according to which the RIS receives non-orthogonal training pilot sequences from two multi-antenna terminals via tunable absorption phase profiles, and then, estimates the respective channels via its signal processing unit, is presented. The channel estimates are particularly used by the RIS controller to design the capacity-achieving reflection phase configuration of the metasurface front end. The proposed channel estimation algorithm, which is based on the Alternating Direction Method of Multipliers (ADMM), profits from the RIS random spatial absorption sampling to capture the entire signal space, and exploits the beamspace sparsity and low-rank properties of extremely large MIMO channels, which is particularly relevant for communication systems at the FR3 band and above. Our extensive numerical investigations showcase the superiority of the proposed channel estimation technique over benchmark schemes for various system and RIS hardware configuration parameters, as well as the effectiveness of using channel estimates at the RIS side to dynamically optimize the possibly phase-quantized reflection coefficients of its unit elements.

Problem

• Enabling channel estimation for RIS in MIMO systems
• Optimizing RIS reflection coefficients via ADMM algorithm
• Improving performance with non-orthogonal pilot sequences

Innovation

• Semi-passive RIS with RF chains for pilot signals
• ADMM-based channel estimation using spatial sampling
• Dynamic optimization of RIS reflection coefficients