High-Performance Estimation of Jamming Covariance Matrix for IRS-Aided Directional Modulation Network With a Malicious Attacker

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 by using the statistical properties of the jamming rece...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2022-09, Vol.71 (9), p.10137-10142
Main Authors: He, Hangjia, Su, Ting, Wang, Hongjun, Teng, Yin, Shi, Weiping, Shu, Feng, Wang, Jiangzhou
Format: Article
Language:English
Subjects:
Array signal processing
Beamforming
Clutter
Complexity theory
Covariance matrices
Covariance matrix
Covariance matrix estimation
directional modulation
Eigenvalues
Estimation
intelligent reflecting surface
Jamming
malicious attacker
Modulation
Optimization
Parameter estimation
Wireless communication
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Summary: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 by using the statistical properties of the jamming received by Bob. Thus, it is very necessary to estimate the receive jamming covariance matrix (JCM) at Bob. To achieve a precise JCM estimation, 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 derived according to the rank-2 constraint of JCM. The PEM-GD method fully explores the structure features of JCM and the PEM-AO is proposed to decrease the computational complexity of the former via dimensionality reduction. The simulation results show that the proposed three methods perform better than directly using sample covariance matrix. Additionally, the proposed PEM-GD and PEM-AO outperform EVD method and the clutter and disturbance covariance estimator RCML.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3177665