Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems

TL;DR

This paper proposes a sparse channel estimation method for IRS-assisted mmWave systems that significantly reduces training overhead while maintaining accurate beamforming capabilities.

Contribution

It introduces a novel sparse representation approach leveraging matrix properties to efficiently estimate channels in IRS-assisted mmWave systems.

Findings

Accurate channel estimation achieved with reduced training overhead

Sparse representation effectively captures cascade channel structure

Method outperforms traditional estimation techniques in simulations

Abstract

In this paper, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction.