论文标题
真正智能反映表面辅助的安全沟通使用深度学习
Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning
论文作者
论文摘要
本文认为在充满挑战的无线环境中进行通信的物理层安全设计的机器学习。假定无线电环境可以借助基于元材料的智能反射表面（IRS）进行编程，从而允许可自定义的路径损失，多路褪色和干扰效果。特别是，利用来自IRS元素的细粒度反射来创建通道优势，以最大程度地提高合法接收器的保密率。已经开发了一种深度学习（DL）技术，以实时调整IRS元素的反思。仿真结果表明，DL方法与常规方法相当，同时显着降低了计算复杂性。
This paper considers machine learning for physical layer security design for communication in a challenging wireless environment. The radio environment is assumed to be programmable with the aid of a meta material-based intelligent reflecting surface (IRS) allowing customisable path loss, multi-path fading and interference effects. In particular, the fine-grained reflections from the IRS elements are exploited to create channel advantage for maximizing the secrecy rate at a legitimate receiver. A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time. Simulation results demonstrate that the DL approach yields comparable performance to the conventional approaches while significantly reducing the computational complexity.
