Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications

By Helin Yang, Zehui Xiong, Jun Zhao, Dusist Niyato, Liang Xiao, Qingqing Wu. IEEE Transactions on Wireless Communications, 2020

410 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/98f4cab1e7834612da2621211d4092820b60d963

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Citations in the corpus

Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, background): “More recently, a deep reinforcement learning approach is developed in [350] to maximize the minimum secrecy rate”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead (2020, background): “In [276], the authors study an RIS-aided wireless communication system for physical layer security, in which an RIS is deployed to adjust its surface reflecting elements in order to guarantee the secure communication of multiple”
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial (2020, methodology): “methods are shown to be more effective than the alternating optimization algorithms [127].”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead (2020, background): “In [278], the authors study an RIS-aided wireless communication system for physical layer security, in which an RIS is deployed to adjust its surface reflecting elements in order”
Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, methodology): “[130] considered the application of RIS to physical layer security.”
Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial (2020, background): “, [79], [118]–[125]), even for the challenging scenario when the conventional PHY-layer security techniques are ineffective [9], [118].”
Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications (2019, background): “In [12], [13], the authors reported some important and interesting results in RIS-aided secure communications.”
An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems (2021, neutral): “Examples of application of deep reinforcement learning to the optimization of RIS-aided communications can be found in [138]–[140].”