Max-Min Fairness for IRS-Assisted Secure Two-Way Communications

TL;DR

This paper proposes an optimization framework for IRS-assisted secure two-way communication systems to maximize the minimum secrecy rate among users, demonstrating significant performance gains over baseline schemes.

Contribution

It introduces a novel alternating optimization approach combining fractional programming and semi-definite programming for IRS phase shift and power control.

Findings

Achieves up to 3.6-fold increase in minimum secrecy rate.

Effective in scenarios with malicious users close to legitimate users.

Performance validated across multiple user configurations.

Abstract

This paper investigates an intelligent reflective surface (IRS) assisted secure multi-user two-way communication system. The aim of this paper is to enhance the physical layer security by optimizing the minimum secrecy-rate among all user-pairs in the presence of a malicious user. The optimization problem is converted into an alternating optimization problem consisting of two sub-problems. Transmit power optimization is handled using a fractional programming method, whereas IRS phase shift optimization is handled with semi-definite programming. The convergence of the proposed algorithm is investigated numerically. The performance gain in minimum secrecy-rate is quantified for four different user configurations in comparison to the baseline scheme. Results indicate a 3.6-fold gain in minimum secrecy rate over the baseline scheme when the IRS is positioned near a legitimate user, even when the malicious user is located close to the same legitimate user.