articleIEEE Transactions on Wireless CommunicationsSep 25, 2020GREEN OA

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

HYHelin YangZXZehui XiongJZJun ZhaoDNDusit NiyatoLXLiang Xiao

Nanyang Technological University · Xiamen University · +2 more institutions

Indexed inarxivcrossref

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…

Citation impact

486
total citations
FWCI
29.86
Percentile
100%
References
40
Citations per year

Authors

6
  • HY
    Helin YangCorresponding

    Nanyang Technological University

  • ZX
    Zehui Xiong

    Nanyang Technological University

  • JZ
    Jun Zhao

    Nanyang Technological University

  • DN
    Dusit Niyato

    Nanyang Technological University

  • LX
    Liang Xiao

    Xiamen University

Topics & keywords

Topics
Keywords
  • Beamforming
  • Reinforcement learning
  • Base station
  • Wireless
  • Secrecy
  • Channel (broadcasting)
  • Quality of service
  • Channel state information
No related works found for this paper.

Funding