Efficient Trajectory Planning and Dynamic Resource Allocation for UAV-Enabled MEC System

This letter aims to minimize the summation energy consumption of user equipments under controllable task backlog queues with unmanned aerial vehicles (UAV)-assisted mobile edge computing system. Based on Lyapunov optimization, the original stochastic optimization problem can be transformed into a deterministic optimization problem, and system stability is also ensured. To address this nonconvex problem, we employ alternating optimization to achieve the near-optimal transmit power, bandwidth ratio, and trajectory planning, respectively. Furthermore, considering the accuracy of the solution and algorithm complexity, we propose a two-stage operating mode for the UAV based on a recursive successive convex approximation method to optimize trajectory. Finally, the convergence and effectiveness of the proposed algorithm are validated through simulation results.