Joint Optimization for Aerial Intelligent Reflecting Surface Assisted Secure Wireless Network

This paper investigates the problem of average secrecy rate maximization for an aerial intelligent reflecting surface (AIRS)-assisted downlink wireless communication system with multiple users and a single eavesdropper, where the AIRS consists of an unmanned aerial vehicle (UAV) equipped with an intelligent reflecting surface (IRS). Our goal is to maximize the minimum average secrecy rate by jointly optimizing the phase shift of the IRS, the communication scheduling, and the trajectory of the UAV. To tackle the above problem, we develop an alternating optimization (AO) algorithm, where the semidefinite relaxation (SDR) and the successive convex approximation (SCA) techniques are used to tackle the non-convex terms. Simulation results show that the proposed algorithm can effectively improve the minimum average secrecy rate.