In this article, physical layer security (PLS) in an intelligent reflecting surface (IRS) assisted multiple-input multiple-output multiple antenna eavesdropper (MIMOME) system is studied. In particular, we consider a practical scenario without instantaneous channel state information (CSI) of the eavesdropper and assume that the eavesdropping channel is a Rayleigh channel. To reduce the complexity of currently available IRS-assisted PLS schemes, we propose a low-complexity deep learning (DL) based approach to design transmitter beamforming and IRS jointly, where the precoding vector and phase shift matrix are designed to minimize the secrecy outage probability. Simulation results demonstrate that the proposed DL-based approach can achieve a similar performance of that with conventional alternating optimization (AO) algorithms for a significant reduction in the computational complexity.
翻译：本文研究了智能反射表面（Intelligent Reflecting Surface，IRS）辅助下多输入多输出多天线窃听者（Multiple-Input Multiple-Output Multiple Antenna Eavesdropper，MIMOME）系统中的物理层安全（Physical Layer Security，PLS）。特别地，我们考虑了一个实际场景，其中缺乏窃听者瞬时信道状态信息（Channel State Information，CSI），并假设窃听信道为瑞利信道。为了减少当前可用的IRS辅助PLS方案的复杂性，我们提出了一种基于深度学习（Deep Learning，DL）的低复杂度方法来联合设计发射波束成形和IRS，其中预编码向量和相移矩阵被设计为最小化秘密失效概率。仿真结果表明，与传统交替优化（Alternating Optimization，AO）算法相比，所提出的基于DL的方法可以实现类似的性能，同时显著降低了计算复杂度。
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