Intelligent Reflecting Surface: A Programmable Wireless Environment for Physical Layer Security
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Abstract (arXiv)
In this paper, we introduce an intelligent reflecting surface (IRS) to provide a programmable wireless environment for physical layer security. By adjusting the reflecting coefficients, the IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in a desired way toward the intended receiver. Specifically, we consider a downlink multiple-input single-output (MISO) broadcast system where the base station (BS) transmits independent data streams to multiple legitimate receivers and keeps them secret from multiple eavesdroppers. By jointly optimizing the beamformers at the BS and reflecting coefficients at the IRS, we formulate a minimum-secrecy-rate maximization problem under various practical constraints on the reflecting coefficients. The constraints capture the scenarios of both continuous and discrete reflecting coefficients of the reflecting elements. Due to the non-convexity of the formulated problem, we propose an efficient algorithm based on the alternating optimization and the path-following algorithm to solve it in an iterative manner. Besides, we show that the proposed algorithm can converge to a local (global) optimum. Furthermore, we develop two suboptimal algorithms with some forms of closed-form solutions to reduce the computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 May 2019.
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Citing papers per year: 2022: 3.
3 papers.
- 2022A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systemscited by 18
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
- 2022Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methodscited by 0
- 2022A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems2 Dec 2022
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces8 May 2022
- 2022Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods28 Apr 2022