Exploiting Structured Sparsity with Low Complexity Sparse Bayesian Learning for RIS-assisted MIMO Channel Estimation

TL;DR

This paper introduces a novel low-complexity Bayesian learning algorithm for RIS-assisted MIMO channel estimation, leveraging structured sparsity to improve accuracy and efficiency in 6G network scenarios.

Contribution

It proposes the UAMPSBL-PCI algorithm that exploits structured sparsity with reduced complexity, outperforming existing methods in RIS-assisted MIMO channel estimation.

Findings

Enhanced estimation accuracy in simulations

Lower computational complexity compared to existing algorithms

Robust performance across various environments

Abstract

As an emerging communication auxiliary technology, reconfigurable intelligent surface (RIS) is expected to play a significant role in the upcoming 6G networks. Due to its total reflection characteristics, it is challenging to implement conventional channel estimation algorithms. This work focuses on RIS-assisted MIMO communications. Although many algorithms have been proposed to address this issue, there are still ample opportunities for improvement in terms of estimation accuracy, complexity, and applicability. To fully exploit the structured sparsity of the multiple-input-multiple-output (MIMO) channels, we propose a new channel estimation algorithm called unitary approximate message passing sparse Bayesian learning with partial common support identification (UAMPSBL-PCI). Thanks to the mechanism of PCI and the use of UAMP, the proposed algorithm has a lower complexity while delivering enhanced performance relative to existing channel estimation algorithms. Extensive simulations demonstrate its excellent performance in various environments.