PPO-based deployment and phase control for movable intelligent reflecting surface

Abstract

Abstract Intelligent reflecting surface (IRS) stands as a promising technology to revolutionize wireless communication by manipulating incident signal amplitudes and phases to enhance system performance. While existing research primarily centers around optimizing the phase shifts of IRS, the deployment of IRS on movable platforms introduces a new degree of freedom in the design of IRS-assisted systems. Leveraging flexible deployment strategies for IRS holds the potential to further amplify network throughput and extend coverage. This paper addresses the challenging non-convex joint optimization problem of the movable IRS and proposes a dynamic optimization algorithm based on proximal policy optimization (PPO) for dynamically optimizing the aerial position and phase configuration of IRS. Simulation results show the effectiveness of the proposed approach, demonstrating significant performance improvements compared to communication schemes without IRS assistance and conventional static IRS-assisted methods.

Keywords

• Intelligent reflecting surface (IRS)
• Deep reinforcement learning (DRL)
• Movable deployment