Cost-Efficient RIS-Aided Channel Estimation via Rank-One Matrix Factorization

TL;DR

This paper introduces a cost-efficient RIS-aided channel estimation method using rank-one matrix factorization, significantly reducing training overhead in large-scale wireless systems.

Contribution

It proposes a novel matrix factorization approach for RIS channel estimation, lowering computational and training costs compared to existing methods.

Findings

Reduces training overhead substantially.

Effective in estimating RIS channels with large element counts.

Numerical simulations validate the method's performance.

Abstract

A reconfigurable intelligent surface (RIS) consists of massive meta elements, which can improve the performance of future wireless communication systems. Existing RIS-aided channel estimation methods try to estimate the cascaded channel directly, incurring high computational and training overhead especially when the number of elements of RIS is extremely large. In this paper, we propose a cost-efficient channel estimation method via rank-one matrix factorization (MF). Specifically, if the RIS is employed near base station (BS), it is found that the RIS- aided channel can be factorized into a product of low-dimensional matrices. To estimate these factorized matrices, we propose alternating minimization and gradient descent approaches to obtain the near optimal solutions. Compared to directly estimating the cascaded channel, the proposed MF method reduces training overhead substantially. Finally, the numerical simulations show the effectiveness of the proposed MF method.