Intelligent Reflecting Surface: A Programmable Wireless Environment for Physical Layer Security

TLDR

The paper proposes using an intelligent reflecting surface to create a programmable wireless environment that enhances physical layer security. The authors model a downlink MISO broadcast system and jointly optimize base‑station beamformers and IRS reflecting coefficients—subject to continuous or discrete coefficient constraints—using alternating optimization and a path‑following algorithm, and also propose two suboptimal closed‑form solutions. Simulations demonstrate that the proposed algorithms converge to optimal solutions and that the IRS significantly improves secrecy performance.

Abstract

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 the 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 computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms.