A Fast UAV Trajectory Planning Framework in RIS-assisted Communication Systems with Accelerated Learning via Multithreading and Federating

Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communications have been realized as essential to space-air-group system integration in the 6G technology landscape. Trajectory planning plays a crucial role in RIS-assisted UAV communications to face the challenges of UAV's limited power capacities and dynamic wireless channels. Existing solutions assume complete channel state information, focus on single-rotor UAVs, and rely heavily on time-consuming training processes for machine learning; thus, they lack applicability to deal with highly dynamic real-world scenarios. To fill these research gaps, we aim to characterize RISassisted UAV communications and design responsive and accurate UAV trajectory planning algorithms in this paper. We first develop a communication model with incomplete information and an energy consumption model for quadrotor UAVs. We then formulate UAV trajectory planning as an optimization problem to minimize UAV's energy consumption while maintaining communication throughput. To solve this problem, we design an acceleration framework, FedX, for reinforcement learning (RL) solvers and present two fast trajectory planning algorithms, FedSAC and FedPPO, as instantiations of the FedX framework. Our evaluation results indicate that the proposed framework is effective and efficient-more than 3 times faster with 5 agents and 7 times faster with 10 agents than standard RL algorithms, making it suitable for using RL solvers within wireless networks and mobile computing environments. We also discuss and identify the pros and cons of our proposed framework.