Paper Info
Title
Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications
Abstract
In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the 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 considering different quality of service (QoS) requirements and time-varying channel conditions. 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, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty 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.
Year
DOI
Venue
2021
10.1109/TWC.2020.3024860
IEEE Transactions on Wireless Communications
Keywords
DocType
Volume
Secure communication,intelligent reflecting surface,beamforming,secrecy rate,deep reinforcement learning
Journal
20
Issue
ISSN
Citations 
1
1536-1276
44
PageRank 
References 
Authors
0.93
23
5
Name
Order
Citations
PageRank
Yang Helin1722.02
Zehui Xiong258654.94
Jun Zhao319819.77
Niyato Dusit49486547.06
Liang Xiao590977.16