High-performance Estimation of Jamming Covariance Matrix for IRS-aided Directional Modulation Network with a Malicious Attacker

TL;DR

This paper introduces three high-performance methods for accurately estimating the jamming covariance matrix in IRS-assisted directional modulation systems to improve anti-jamming capabilities against malicious attacks.

Contribution

It proposes three novel estimation techniques—EVD, PEM-GD, and PEM-AO—for precise JCM estimation, enhancing anti-jamming performance in IRS-aided directional modulation networks.

Findings

Proposed methods outperform existing sample covariance matrix in low/medium JNR.

PEM-GD and PEM-AO outperform EVD and RCML in simulations.

Methods achieve better accuracy in challenging jamming conditions.

Abstract

In this paper, we investigate the anti-jamming problem of a directional modulation (DM) system with the aid of intelligent reflecting surface (IRS). As an efficient tool to combat malicious jamming, receive beamforming (RBF) is usually designed to be on null-space of jamming channel or covariance matrix from Mallory to Bob. Thus, it is very necessary to estimate the receive jamming covariance matrix (JCM) at Bob. To achieve a precise JCM estimate, three JCM estimation methods, including eigenvalue decomposition (EVD), parametric estimation method by gradient descend (PEM-GD) and parametric estimation method by alternating optimization (PEM-AO), are proposed. Here, the proposed EVD is under rank-2 constraint of JCM. The PEM-GD method fully explores the structure features of JCM and the PEM-AO is to decrease the computational complexity of the former via dimensionality reduction. The simulation results show that in low and medium jamming-noise ratio (JNR) regions, the proposed three methods perform better than the existing sample covariance matrix method. The proposed PEM-GD and PEM-AO outperform EVD method and existing clutter and disturbance covariance estimator RCML.