论文标题
大规模MIMO系统中针对恶意IRS的上行链路通道估计和信号提取
Uplink Channel Estimation and Signal Extraction Against Malicious IRS in Massive MIMO System
论文作者
论文摘要
本文研究了反映表面（IRS）的恶意智能的效果。恶意IRS用于通过将合法用户的数据序列（LUS）随机反射到基站（BS）来进行攻击。我们发现LU的数据序列与恶意IRS反映的信号相关。相关性破坏了传统特征值分解（EVD）基于基于的频道估计（CE）方法的性能。为了应对这一挑战，我们在存在恶意IRS的情况下提出了一种基于经验分布的渠道估计方法。提出的方法是通过根据其经验分布来捕获受害IRS的信号中所需的凸壳的作用。仿真结果表明，我们提出的方法在标准化的均方根误差（NMSE）中，我们提出的方法的表现优于传统的基于EVD的方法。
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).
