Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO

TL;DR

This paper introduces a novel sparse Bayesian learning-based method for efficient channel estimation in IRS-aided MIMO systems, exploiting angular sparsity and Kronecker structure to improve accuracy and speed.

Contribution

It proposes two new algorithms for cascaded channel estimation in IRS-MIMO systems using sparse Bayesian learning, addressing non-convex optimization challenges.

Findings

Superior accuracy over existing methods

Faster run time in simulations

Effective exploitation of channel sparsity

Abstract

This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve the problem using the sparse Bayesian learning framework which leads to a non-convex optimization problem. We offer two solution techniques to the problem based on alternating minimization and singular value decomposition. Our simulation results illustrate the superior performance of our methods in terms of accuracy and run time compared with the existing works.