Receiving RISs: Enabling Channel Estimation and Autonomous Configuration

TL;DR

This paper proposes a hardware architecture and a novel channel estimation protocol for semi-passive RISs to enhance MIMO system performance, utilizing ADMM-based algorithms and exploiting channel sparsity for improved estimation accuracy.

Contribution

It introduces a new RIS receiver architecture with a channel estimation protocol that leverages non-orthogonal pilots and beamspace properties, advancing RIS-based MIMO communication capabilities.

Findings

The proposed estimation method outperforms benchmark schemes.

Channel estimates enable dynamic optimization of RIS reflection coefficients.

The approach is effective for large MIMO channels at high-frequency bands.

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.