Secure Transmission for Intelligent Reflecting Surface Assisted Communication with Deep Learning

Xiangyu Zou, Ming Chen, Chongwen Huang, Kezhi Wang, Mohammad Shikh-Bahaei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

This paper investigates the secure transmission for intelligent reflecting surface (IRS) assisted wireless communication systems. Our aim is to maximize the secure energy efficiency of the system via jointly optimizing the IRS phase reflector as well as the number of IRS elements. To solve this non-convex maximization problem, a deep learning (DL) based algorithm is proposed. The proposed deep neural network has fully connected layers and can predict the optimal IRS reflection vector and the optimal number of IRS reflection elements with low complexity. Simulation results show that the proposed DL method achieves higher secure energy efficiency than the conventional methods.

Original languageEnglish
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
DOIs
Publication statusPublished - Dec 2020
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 7 Dec 202011 Dec 2020

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
Country/TerritoryTaiwan, Province of China
CityVirtual, Taipei
Period7/12/2011/12/20

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