Energy efficiency optimization of aerial intelligent reflecting surface-assisted communications based on multi-agent deep reinforcement learning

Abstract

Reconfigurable Intelligent Surface (RIS) technology has emerged as a promising solution, garnering extensive attention in millimeter-wave (mmWave) communication systems. This paper explores the application of deploying aerial reconfigurable intelligent surfaces (ARISs) on multiple unmanned aerial vehicles (UAVs) to provide services to ground users in complex environments. However, existing studies seldom address the orientation design of RISs. In fact, the orientation design of multiple ARISs facilitates the establishment of collaborative communication links for users and enhances communication coverage. Therefore, this paper proposes a joint optimization problem for the trajectories, orientations, and phase shifts of ARISs in multi-ARIS-assisted communication systems, aiming to maximize the system’s energy efficiency. To address this issue, a multi-agent deep reinforcement learning (MADRL) approach, namely multi-agent proximal policy optimization (MAPPO), is employed. Simulation results demonstrate that the proposed scheme enhances system energy efficiency by approximately 22 % over the benchmark scheme.