Channel-Aware User Association and Trajectory Design for Multi-IRS Assisted Multi-UAV Communications

The integration of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs) is promising for providing flexible and intelligent communications to users in urban areas. Existing studies are founded either on the complete Line of Sight (LoS) or complete Non-LoS (NLoS) communication scenarios, while ignoring their coexistence. To solve the above challenge in complicated and dynamic communication scenarios, we formulate an average system sum rate maximization problem with the optimization of joint IRS-user association, multi-UAV trajectory optimization, IRS phase shifts and transmit power allocation. Since the highly complex and coupled variables, we propose a Multi-Agent Deep Reinforcement Learning (MADRL)-based scheme to maximize the average system sum rate. First, we derive two composite channel power gains for different communication conditions. Then, phase alignment theory is utilized to obtain optimal phase control. To guarantee long-term optimization, we propose a scheme based on Multi-Agent Proximal Policy Optimization (MAPPO) and Successive Convex Approximation (SCA) method to jointly optimize multi-UAV trajectories, multi-IRS association and transmit power allocation. Finally, experimental results reveal that the proposed MGBA shows considerable advantages in both the convergence speed and the average system sum rate.