RSRP Measurement Based Channel Autocorrelation Estimation for IRS-Aided Wideband Communication

TL;DR

This paper introduces a neural network-based method for estimating IRS channel autocorrelation matrices in wideband OFDM systems using only RSRP measurements, enhancing IRS reflection optimization without extra pilots.

Contribution

It proposes a novel neural network framework that estimates channel autocorrelation matrices solely from RSRP measurements, improving IRS reflection design in wideband channels.

Findings

Significant performance improvement over existing power-measurement-based IRS designs.

Effective IRS reflection optimization based on estimated autocorrelation matrices.

Validated through numerical simulations in wideband OFDM scenarios.

Abstract

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing powermeasurement-based IRS reflection designs in wideband channels.