A Decoupled Channel Estimation Method for Beyond Diagonal RIS

TL;DR

This paper introduces a novel decoupled channel estimation method for beyond diagonal RIS that improves accuracy by exploiting the Kronecker structure of the combined channel, addressing the complexity of BD-RIS.

Contribution

It proposes a decoupled estimation algorithm using Khatri-Rao factorization to separately estimate channels in BD-RIS, enhancing accuracy over traditional methods.

Findings

The proposed method outperforms classical least squares in accuracy.

Numerical results confirm improved channel estimation precision.

The approach effectively handles the complex structure of BD-RIS channels.

Abstract

Beyond diagonal reconfigurable intelligent surface (BD-RIS) is a new architecture for RIS where elements are interconnected to provide more wave manipulation flexibility than traditional single connected RIS, enhancing data rate and coverage. However, channel estimation for BD-RIS is challenging due to the more complex multiple-connection structure involving their scattering elements. To address this issue, this paper proposes a decoupled channel estimation method for BD-RIS that yields separate estimates of the involved channels to enhance the accuracy of the overall combined channel by capitalizing on its Kronecker structure. Starting from a least squares estimate of the combined channel and by properly reshaping the resulting filtered signal, the proposed algorithm resorts to a Khatri-Rao Factorization (KRF) method that teases out the individual channels based on simple rank-one matrix approximation steps. Numerical results show that the proposed decoupled channel estimation yields more accurate channel estimates than the classical least squares scheme.