Secure Energy Efficiency in Communication Systems Based on Deep Learning

ZOU Xiangyu; HUANG Chongwen; XU Yongjun; YANG Zhaohui; CAO Yue

doi:10.11999/JEIT211611

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2245-2252

ZOU Xiangyu, HUANG Chongwen, XU Yongjun, YANG Zhaohui, CAO Yue. Secure Energy Efficiency in Communication Systems Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2245-2252. doi: 10.11999/JEIT211611

Citation:

ZOU Xiangyu, HUANG Chongwen, XU Yongjun, YANG Zhaohui, CAO Yue. Secure Energy Efficiency in Communication Systems Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2245-2252. doi: 10.11999/JEIT211611

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Secure Energy Efficiency in Communication Systems Based on Deep Learning

doi: 10.11999/JEIT211611

1.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2.
Zhejiang University, Hangzhou 310007, China
3.
Chongqing University of Posts and Telecommunications, Chongqing 400065, China
4.
University College London, London WC1E7JE, UK
5.
Wuhan University, Wuhan 430072, China

Received Date: 2021-12-30
Rev Recd Date: 2022-06-05

Available Online: 2022-06-07

Publish Date: 2022-07-25

Abstract

Abstract

The secure transmission for Reconfigurable Intelligent Surface (RIS) assisted wireless communication systems is investigated in this paper. In the studied model, one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point. Therefore, it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem. This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined via jointly optimizing the RIS reflection reflector as well as the number of RIS elements, which results in a non-convex optimization problem that is intractable to solve by the traditional methods. To tackle this issue, a new algorithm by leveraging the advance of the emerging Deep Learning (DL) technique is proposed so as to find the near optimal RIS reflection vector and determine the near optimal number of RIS reflection elements. Simulation results show that the proposed method reaches 96% of the near optimal secure energy efficiency of the genie-aided algorithm.
- Reconfigurable Intelligent Surfaces (RIS),
- Physical layer security,
- Deep Learning (DL),
- Secure energy efficiency

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References(33)

References

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