Channel Estimation for XL-IRS Assisted Wireless Systems with Double-sided Visibility Regions

TL;DR

This paper proposes an efficient three-step channel estimation method for XL-IRS assisted multi-user systems, addressing near-field effects and double-sided visibility regions to improve estimation accuracy and reduce complexity.

Contribution

It introduces a novel three-step estimation scheme that leverages VR detection and a common BS-IRS channel to efficiently estimate channels in near-field XL-IRS systems.

Findings

The proposed method outperforms benchmark schemes in estimation accuracy.

VR detection significantly reduces the estimation dimension.

Numerical results validate the effectiveness of the proposed approach.

Abstract

In this paper, we study efficient channel estimation design for an extremely large-scale intelligent reflecting surface (XL-IRS) assisted multi-user communication systems, where both the base station (BS) and users are located in the near-field region of the XL-IRS. Two unique channel characteristics of XL-IRS are considered, namely, the near-field spherical wavefronts and double-sided visibility regions (VRs) at the BS and users, which render the channel estimation for XL-IRS highly challenging. To address this issue, we propose in this paper an efficient three-step XL-IRS channel estimation method. Specifically, in the first step, an anchor node is delicately deployed near the XL-IRS to estimate the cascaded BS-IRS-anchor channel. Then, an efficient VR detection method is devised to estimate the VR information between the BS and XL-IRS. In this way, only the channels from the visible XL-IRS elements to the BS are estimated, thereby reducing the dimension of the cascaded BS-IRS-users channels to be estimated. Third, by leveraging the common BS-IRS channel, the cascaded channels for all users are consecutively estimated accounting for the VRs of the IRS-user channels. Finally, numerical results are provided to demonstrate the effectiveness of our proposed channel estimation scheme as compared to various benchmark schemes.