Tensor-Based Channel Estimation and Data-Aided Tracking in IRS-Assisted MIMO Systems

TL;DR

This paper introduces a tensor-based method for channel estimation and data tracking in IRS-assisted MIMO systems, effectively handling channel aging to improve detection accuracy and system performance.

Contribution

It presents a novel tensor modeling approach for joint channel estimation and IRS configuration, along with a data-aided tracking scheme for aging channels in IRS-assisted MIMO systems.

Findings

Improved bit error rate over existing methods

Reduced mean squared error in data estimation

Effective tracking of channel aging processes

Abstract

This letter proposes a model for symbol detection in the uplink of IRS-assisted networks in the presence of channel aging. During the first stage, we model the received pilot signal as a tensor, which serves as a basis for both estimating the channel and configuring the IRS. In the second stage, the proposed tensor approach tracks the aging process to detect and estimate the transmitted data symbols. Our evaluations show that our proposed channel and symbol estimation schemes improve the performance of IRS-assisted systems in terms of the achieved bit error rate and mean squared error of the received data, compared to state of the art schemes.