Deep Reinforcement Learning-Based Secure Standalone Intelligent Reflecting Surface Operation

Research output: Contribution to conference typesPaperpeer-review

1 Citation (Scopus)

Abstract

In this paper, we investigate secure wireless commu-nication in an intelligent reflecting surface (IRS)-assisted system where the IRS is used to secure the communication of one legitimate receiver in presence of an eavesdropper. We assume that the IRS is standalone, i.e. the passive beamforming of the IRS is carried out completely on its own. Thus, we design an IRS with several passive elements and only two RF chains that can obtain a partial channel state information (CSI) among each node and the IRS. The partial CSI is then mapped into full CSI by using the correlation information between the channels of different IRS elements. We develop a deep reinforcement learning (DRL)-based framework using the deep deterministic policy gradient (DDPG) algorithm to obtain the IRS beamforming vector resulting in maximizing the secrecy rate. Numerical results demonstrate the ability of this technique to secure the wireless communication system.

Original languageEnglish
Pages5329-5334
Number of pages6
DOIs
Publication statusPublished - 2022
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: 4 Dec 20228 Dec 2022

Conference

Conference2022 IEEE Global Communications Conference, GLOBECOM 2022
Country/TerritoryBrazil
CityVirtual, Online
Period4/12/20228/12/2022

Keywords

  • DDPG
  • Deep Reinforcement Learning
  • Intelligent Reflecting Surface
  • Physical layer security
  • Reconfigurable Intelligent Surface

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