User Power Measurement Based IRS Channel Estimation via Single-Layer Neural Network

TL;DR

This paper introduces a novel single-layer neural network method for estimating IRS channels using only user received power measurements, reducing training overhead and enhancing multiuser communication performance.

Contribution

It presents a new neural network-based IRS channel estimation technique that relies solely on power measurements, simplifying implementation and improving system performance.

Findings

Significant improvement in minimum user SNR with the proposed method.

Reduced training overhead compared to traditional channel estimation methods.

Effective IRS reflection design based on estimated channels.

Abstract

One main challenge for implementing intelligent reflecting surface (IRS) aided communications lies in the difficulty to obtain the channel knowledge for the base station (BS)-IRS-user cascaded links, which is needed to design high-performance IRS reflection in practice. Traditional methods for estimating IRS cascaded channels are usually based on the additional pilot signals received at the BS/users, which increase the system training overhead and also may not be compatible with the current communication protocols. To tackle this challenge, we propose in this paper a new single-layer neural network (NN)-enabled IRS channel estimation method based on only the knowledge of users' individual received signal power measurements corresponding to different IRS random training reflections, which are easily accessible in current wireless systems. To evaluate the effectiveness of the proposed channel estimation method, we design the IRS reflection for data transmission based on the estimated cascaded channels in an IRS-aided multiuser communication system. Numerical results show that the proposed IRS channel estimation and reflection design can significantly improve the minimum received signal-to-noise ratio (SNR) among all users, as compared to existing power measurement based designs.