Broadcast Secrecy Rate Maximization in UAV-Empowered IRS Backscatter Communications

Abstract

The backscatter communications (BackCom) and physical layer security are respected to realize extremely low-power secure communications in the imminent sixth generation (6G). In a BackCom system, the backscatter device without radio frequency components sends messages to users by reflecting the external signals. However, the double-fading effect limits BackCom’s performance and the commonly used broadcast mode is vulnerable to eavesdropping. Two promising technologies, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV), show excellent potential in handling these problems. In this paper, we propose a UAV-empowered IRS-BackCom network, where an IRS acts as the backscatter device and uses the received signals from a UAV for BackCom. We aim to guarantee secure transmission and maximize the broadcast secrecy rate by jointly optimizing the UAV’s beamformer and trajectory and the IRS’s reflection coefficient. To tackle the non-convex problem, we leverage the block coordinate descent method to decompose it into three subproblems. Specifically, the UAV’s beamformer and trajectory and the IRS’s reflection matrix are optimized alternatively. Further, we adopt reinforcement learning to facilitate the intractable UAV’s trajectory optimization. Simulation results verify the feasibility and effectiveness of the proposed system model and the optimization scheme.