Physical Layer Security in NOMA-Based VLC Systems With Optical Intelligent Reflecting Surface: A Max-Min Secrecy Data Rate Perspective

Optical intelligent reflecting surface (OIRS) is a promising technology in visible light communications (VLCs), which can help VLC overcome the shortcoming of being susceptible to occlusion. It is shown that OIRS has many advantages for nonorthogonal multiple access (NOMA)-based VLC due to its ability to reconfigure optical wireless channels. In this article, we propose an effective OIRS-aided physical layer security (PLS) scheme for NOMA-based VLC networks against multiple eavesdroppers (Eves). By exploiting artificial noise (AN) to jam Eves, the maximization problem of the minimum achievable secrecy data rate is investigated, subject to successive interference cancellation (SIC) decoding conditions and OIRS constraints. Specifically, the original problem is decomposed into the power allocation and OIRS configuration subproblems by a block coordinate descent (BCD) algorithm. Moreover, the semi-definite relaxation and successive convex approximation are employed to solve the subproblems, after which a stochastic probability assignment method is adopted for the integer OIRS constraint. Finally, simulation results demonstrate that AN can enhance the system performance in most scenarios, and the minimum achievable secrecy data rate can be significantly improved by the proposed algorithm, demonstrating the potential of OIRS for enhancing PLS in optical wireless communications.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-11-07
DOI
https://doi.org/10.1109/jiot.2024.3493858
Citations
12
Primary Topic
Optical Wireless Communication Technologies
Type
article
Field-Weighted Citation Impact
2.50

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article

Physical Layer Security in NOMA-Based VLC Systems With Optical Intelligent Reflecting Surface: A Max-Min Secrecy Data Rate Perspective

Shiyuan Sun, Fang Yang, Zhu Han, Jian Song et al.
12 citations
IEEE Internet of Things Journal
Optical Wireless Communication Technologies
2.50
article

Physical Layer Security in NOMA-Based VLC Systems With Optical Intelligent Reflecting Surface: A Max-Min Secrecy Data Rate Perspective

Shiyuan Sun, Fang Yang, Zhu Han, Jian Song, Zehao Liu
article en
12 citations

Abstract

Optical intelligent reflecting surface (OIRS) is a promising technology in visible light communications (VLCs), which can help VLC overcome the shortcoming of being susceptible to occlusion. It is shown that OIRS has many advantages for nonorthogonal multiple access (NOMA)-based VLC due to its ability to reconfigure optical wireless channels. In this article, we propose an effective OIRS-aided physical layer security (PLS) scheme for NOMA-based VLC networks against multiple eavesdroppers (Eves). By exploiting artificial noise (AN) to jam Eves, the maximization problem of the minimum achievable secrecy data rate is investigated, subject to successive interference cancellation (SIC) decoding conditions and OIRS constraints. Specifically, the original problem is decomposed into the power allocation and OIRS configuration subproblems by a block coordinate descent (BCD) algorithm. Moreover, the semi-definite relaxation and successive convex approximation are employed to solve the subproblems, after which a stochastic probability assignment method is adopted for the integer OIRS constraint. Finally, simulation results demonstrate that AN can enhance the system performance in most scenarios, and the minimum achievable secrecy data rate can be significantly improved by the proposed algorithm, demonstrating the potential of OIRS for enhancing PLS in optical wireless communications.

IEEE Internet of Things JournalVol. 12(6)
University of Houston (US), Tsinghua University (CN)
National Key Research and Development Program of China, Division of Computer and Network Systems
Openalex Percentile: Top 10%
Optical Wireless Communication Technologies
2.50
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Physical Layer Security in NOMA-Based VLC Systems With Optical Intelligent Reflecting Surface: A Max-Min Secrecy Data Rate Perspective — Shiyuan Sun, Fang Yang, et al. · IEEE Internet of Things Journal (2024) | TGRS Research Map | TGRS