Online Trajectory Planning and Resource Allocation of UAV-Enabled MEC Networks Empowered by RIS

We consider a mobile edge computing (MEC) framework empowered by unmanned aerial vehicle (UAV) and reflecting intelligent surface (RIS) serving multiple ground users in a practical environment, where mobile ground users generate movements and tasks randomly. Our objective is to optimize energy efficiency while ensuring long-term data queue stability, assuming knowledge of the channel state information. The problem is formulated as a stochastic optimization problem, and the Lyapunov method is applied to convert the initial problem into per-slot problems. Without the future knowledge of user movement, we consider the outage constraint into the per-slot problem to derive robust resource allocation and trajectory design in the MEC system. For each per-slot problem, an alternating optimization algorithm utilizing successive convex approximation technique is designed to solve it. This solution guarantees adherence to the UAV energy budget constraint while achieving a balance between system energy efficiency and the length of the queue backlog. Simulation results demonstrate that the proposed algorithm achieves better performance than other benchmark methods in terms of improving energy efficiency and maintaining queue stability.