Constant-Modulus Secure Analog Beamforming for an IRS-Assisted Communication System With Large-Scale Antenna Array

• Published in: IEEE transactions on information forensics and security Vol. 20; pp. 2957 - 2969
• Main Authors: Xiong, Weijie, Lin, Jingran, Xiao, Zhiling, Li, Qiang
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: analog beamforming; Antenna arrays; Array signal processing; constant modulus constraints; Costs; Eavesdropping; Hardware; intelligent reflecting surface; Large-scale antenna arrays; Manifolds; non-convex optimization; Optimization; physical-layer security; Radio frequency; Reflector antennas; Security
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2025.3550053

Physical layer security (PLS) is an important technology in wireless communication systems to safeguard communication privacy and security between transmitters and legitimate users. The integration of large-scale antenna arrays (LSAA) and intelligent reflecting surfaces (IRS) has emerged as a promising approach to enhance PLS. However, LSAA requires a dedicated radio frequency (RF) chain for each antenna element, and IRS comprises hundreds of reflecting micro-antennas, leading to increased hardware costs and power consumption. To address this, cost-effective solutions like constant modulus analog beamforming (CMAB) have gained attention. This paper investigates PLS in IRS-assisted communication systems with a focus on jointly designing the CMAB at the transmitter and phase shifts at the IRS to maximize the secrecy rate. The resulting secrecy rate maximization (SRM) problem is non-convex. To solve the problem efficiently, we propose two algorithms: 1) the time-efficient Dinkelbach-BSUM algorithm, which reformulates the fractional problem into a series of quadratic programs using the Dinkelbach method and solves them via block successive upper-bound minimization (BSUM), and 2) the product manifold conjugate gradient descent (PMCGD) algorithm, which provides a better solution at the cost of slightly higher computational time by transforming the problem into an unconstrained optimization on a Riemannian product manifold and solving it using the conjugate gradient descent (CGD) algorithm. Simulation results validate the effectiveness of the proposed algorithms and highlight their distinct advantages.