Secure Transmission for Intelligent Reflecting Surface Assisted Communication with Deep Learning

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

doi:10.1109/GCWkshps50303.2020.9367587

Secure Transmission for Intelligent Reflecting Surface Assisted Communication with Deep Learning

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

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 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 language	English
Title of host publication	2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781728173078
ISBN (Print)	9781728173085
DOIs	https://doi.org/10.1109/GCWkshps50303.2020.9367587
Publication status	Published - 11 Dec 2020
Event	2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China Duration: 7 Dec 2020 → 11 Dec 2020

Publication series

Name	2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference	2020 IEEE Globecom Workshops, GC Wkshps 2020
Country/Territory	Taiwan, Province of China
City	Virtual, Taipei
Period	7/12/20 → 11/12/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

deep learning
Intelligent reflecting surface
physical layer security
secure energy efficiency

Access to Document

10.1109/GCWkshps50303.2020.9367587

Cite this

@inproceedings{195567b16f44409aa51dca299f5b5080,

title = "Secure Transmission for Intelligent Reflecting Surface Assisted Communication with Deep Learning",

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.",

keywords = "deep learning, Intelligent reflecting surface, physical layer security, secure energy efficiency",

author = "Xiangyu Zou and Ming Chen and Chongwen Huang and Kezhi Wang and Mohammad Shikh-Bahaei",

note = "Funding Information: ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation of China (NSFC) under grant 61871128 and 62001051 and the Fundamental Research on Foreword Leading Technology of Jiangsu Province under grant BK20192002.; 2020 IEEE Globecom Workshops, GC Wkshps 2020 ; Conference date: 07-12-2020 Through 11-12-2020",

year = "2020",

month = dec,

day = "11",

doi = "10.1109/GCWkshps50303.2020.9367587",

language = "English",

isbn = "9781728173085",

series = "2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings",

publisher = "IEEE",

booktitle = "2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings",

address = "United States",

}

Zou, X, Chen, M, Huang, C, Wang, K & Shikh-Bahaei, M 2020, Secure Transmission for Intelligent Reflecting Surface Assisted Communication with Deep Learning. in 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings., 367587, 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings, IEEE, Piscataway, NJ, 2020 IEEE Globecom Workshops, GC Wkshps 2020, Virtual, Taipei, Taiwan, Province of China, 7/12/20. https://doi.org/10.1109/GCWkshps50303.2020.9367587

Secure Transmission for Intelligent Reflecting Surface Assisted Communication with Deep Learning. / Zou, Xiangyu; Chen, Ming; Huang, Chongwen et al.
2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings. Piscataway, NJ: IEEE, 2020. 367587 (2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Secure Transmission for Intelligent Reflecting Surface Assisted Communication with Deep Learning

AU - Zou, Xiangyu

AU - Chen, Ming

AU - Huang, Chongwen

AU - Wang, Kezhi

AU - Shikh-Bahaei, Mohammad

N1 - Funding Information: ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation of China (NSFC) under grant 61871128 and 62001051 and the Fundamental Research on Foreword Leading Technology of Jiangsu Province under grant BK20192002.

PY - 2020/12/11

Y1 - 2020/12/11

N2 - 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.

AB - 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.

KW - deep learning

KW - Intelligent reflecting surface

KW - physical layer security

KW - secure energy efficiency

UR - https://www.scopus.com/pages/publications/85102955661

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DO - 10.1109/GCWkshps50303.2020.9367587

M3 - Conference contribution

AN - SCOPUS:85102955661

SN - 9781728173085

T3 - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

BT - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

PB - IEEE

CY - Piscataway, NJ

T2 - 2020 IEEE Globecom Workshops, GC Wkshps 2020

Y2 - 7 December 2020 through 11 December 2020

ER -

Secure Transmission for Intelligent Reflecting Surface Assisted Communication with Deep Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

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