Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications

TL;DR

This paper proposes an efficient optimization algorithm to maximize secrecy rate in IRS-assisted multi-antenna systems, addressing physical-layer security with guaranteed convergence and validated through simulations.

Contribution

It introduces a novel alternating optimization algorithm with closed-form solutions for secrecy rate maximization in IRS-assisted systems.

Findings

The proposed algorithm converges guaranteed.

Simulation results show improved secrecy rate.

The method effectively handles non-convex constraints.

Abstract

We investigate transmission optimization for intelligent reflecting surface (IRS) assisted multi-antenna systems from the physical-layer security perspective. The design goal is to maximize the system secrecy rate subject to the source transmit power constraint and the unit modulus constraints imposed on phase shifts at the IRS. To solve this complicated non-convex problem, we develop an efficient alternating algorithm where the solutions to the transmit covariance of the source and the phase shift matrix of the IRS are achieved in closed form and semi-closed forms, respectively. The convergence of the proposed algorithm is guaranteed theoretically. Simulations results validate the performance advantage of the proposed optimized design.