Channel Estimation for Beyond Diagonal Reconfigurable Intelligent Surfaces with Group-Connected Architectures

TL;DR

This paper proposes an optimized channel estimation method for beyond diagonal reconfigurable intelligent surfaces with group-connected architectures, achieving minimal mean square error and verified through simulations.

Contribution

It introduces a novel LS-based channel estimation strategy for BD-RIS with group-connected structures and designs an architecture that attains the theoretical MSE lower bound.

Findings

Proposed LS-based estimation achieves near-optimal MSE.

Designed BD-RIS architecture guarantees MSE lower bound.

Simulation results confirm the effectiveness of the scheme.

Abstract

We study channel estimation for a beyond diagonal reconfigurable intelligent surface (BD-RIS) aided multiple input single output system. We first describe the channel estimation strategy based on the least square (LS) method, derive the mean square error (MSE) of the LS estimator, and formulate the BD-RIS design problem that minimizes the estimation MSE with unique constraints induced by group-connected architectures of BD-RIS. Then, we propose an efficient BD-RIS design which theoretically guarantees to achieve the MSE lower bound. Finally, we provide simulation results to verify the effectiveness of the proposed channel estimation scheme.