论文标题
基于深度强化学习的智能反射表面，用于安全无线通信
Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications
论文作者
论文摘要
在本文中，我们研究了一个智能反射表面（IRS）的无线安全通信系统，以用于物理层安全性，在该系统中，部署了IRS来调整其表面反射元素，以确保在存在多个窃听器的情况下，在存在多个合法用户的情况下安全地通信。旨在提高系统保密速率，鉴于服务质量（QOS）要求（QOS）要求和时变通道状况，因此制定了共同优化基站（BS）光束成形（BS）的设计问题。由于系统高度动态和复杂，解决非凸优化问题是一项挑战，因此首先提出了一种新型的基于基于的深入强化学习（DRL）的安全束成式方法，以实现针对动态环境中窃听的窃听器的最佳光束成型策略。此外，采用决定后（PDS）和优先经验重播（PER）方案可提高学习效率和保密性绩效。具体而言，PDS能够追踪环境动态特征并相应地调整波束形成策略。仿真结果表明，提出的基于学习的深度PDS安全束缚方法可以显着提高IRS辅助安全通信系统中的系统保密率和QoS满意度的概率。
In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its surface reflecting elements to guarantee secure communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated given the different quality of service (QoS) requirements and time-varying channel condition. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning-based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.
