Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning

Yizhuo Song, Muhammad R. A. Khandaker, Faisal Tariq, Kai Kit Wong, Apriana Toding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
5 Downloads (Pure)

Abstract

This paper considers machine learning for physical layer security design for communication in a challenging wireless environment. The radio environment is assumed to be programmable with the aid of a meta material-based intelligent reflecting surface (IRS) allowing customisable path loss, multi-path fading and interference effects. In particular, the fine-grained reflections from the IRS elements are exploited to create channel advantage for maximizing the secrecy rate at a legitimate receiver. A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time. Simulation results demonstrate that the DL approach yields comparable performance to the conventional approaches while significantly reducing the computational complexity.

Original languageEnglish
Title of host publication93rd IEEE Vehicular Technology Conference (VTC2021-Spring)
PublisherIEEE
ISBN (Electronic)9781728189642
DOIs
Publication statusPublished - 15 Jun 2021
Event93rd IEEE Vehicular Technology Conference 2021 - Virtual, Online
Duration: 25 Apr 202128 Apr 2021

Conference

Conference93rd IEEE Vehicular Technology Conference 2021
Abbreviated titleVTC 2021-Spring
CityVirtual, Online
Period25/04/2128/04/21

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning'. Together they form a unique fingerprint.

Cite this