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.

翻译：在本文中,我们在智能反射表面(IRS)的帮助下,调查了方向调制(DM)系统的方向调制(DM)系统的反干扰问题。作为打击恶意干扰的有效工具,接收波束成形(RBF)通常设计在从马洛里到鲍勃的干扰通道或共变矩阵的空格上。因此,非常有必要估计鲍勃接收干扰共变矩阵(JCM)的结构特征。为了实现准确的JCM估计,提出了三项JCM估计方法,包括平值分解(EVD)、梯度降压(PEM-GD)和交替优化(PEM-AO)的参数估测法。在这里,拟议的EVD处于JCM的二级限制之下。PEM-GD方法充分探索了JCM和PEM-AO的结构性结构特征,目的是通过降低度来降低前者的计算复杂性。模拟结果表明,在中低和中位调调调调(JNRR)区域,拟议的三种方法比现有的MMMMM-CMMM方法表现得更好。