Uplink Channel Estimation and Signal Extraction Against Malicious IRS in Massive MIMO System

TL;DR

This paper addresses the challenge of malicious IRS in massive MIMO systems by proposing an empirical-distribution-based channel estimation method that outperforms traditional eigenvalue decomposition techniques, especially under attack conditions.

Contribution

It introduces a novel empirical-distribution-based channel estimation approach to mitigate malicious IRS effects in massive MIMO systems, improving accuracy over traditional methods.

Findings

Proposed method outperforms EVD-based methods by nearly 5 dB NMSE.

Malicious IRS causes correlation that undermines traditional channel estimation.

Empirical distribution approach effectively captures desired signals amidst malicious reflections.

Abstract

This paper investigates effect of malicious intelligence reflecting surface (IRS). The malicious IRS is utilized for performing attack by randomly reflecting data sequences of legitimate users (LUs) to a base station (BS). We find that the data sequences of LUs are correlative to the signals reflected by malicious IRS. The correlation undermines the performance of traditional eigenvalue decomposition (EVD)-based channel estimation (CE) methods. To address this challenge, we propose a empirical-distribution-based channel estimation approach in the presence of malicious IRS. The proposed method works by capturing desired convex hulls from signals disturbed by malicious IRS, on the basis of its empirical distribution. Simulation results show that our proposed approach outperforms traditional EVD-based methods as much as nearly 5 dB in normalized mean square error (NMSE).