Multi-User Secrecy Rate Maximization in IRS-aided Systems
Monir Abughalwa, Diep Ngoc Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz · 2024
Intelligent reflective surfaces (IRS) allow us to actively customize the radio environment by manipulating the reflected signals upon them. Among their various applications, one notable use is enhancing user security and privacy. This is achieved by strategically reflecting signals from the transmitter to improve reception for authorized users while minimizing the signal quality for suspicious eavesdroppers. However, under multiuser settings, given the heterogeneity in users’ channels, locations, and the unknown location of the eavesdropper, it is challenging to avoid secrecy outage for all users. This paper takes the first step in investigating the multi-user secrecy rate (SR) maximization in IRS-aided systems. To this end, we aim to maximize the minimum user’s SR by optimizing the transmitter’s beamforming vector and the IRS’ passive reflective elements (PREs). The resulting problem is non-convex. To tackle this, we first linearize the objective function and decompose the problem into two sequential optimization problems. We then design an alternating optimization (AO) method to jointly optimize the transmitter’s beamforming vector and the IRS’ PREs. We prove that the proposed algorithm converges to a locally optimal solution of the above non-convex optimization problem. Numerical results demonstrate that the proposed Max-Min algorithm can provide secure communication for all the users.