Multi-UAV Enabled MEC Networks: Optimizing Delay Through Intelligent 3-D Trajectory Planning and Resource Allocation

Mobile Edge Computing (MEC) reduces the computational burden on terminal devices by shortening the distance between these devices and computing nodes. Integrating Uncrewed Aerial Vehicles (UAVs) with enhanced MEC networks can leverage the high mobility of UAVs to flexibly adjust network topology, further expanding the applicability of MEC. However, in highly dynamic and complex real-world environments, it is crucial to balance task offloading effectiveness with algorithm performance. This paper investigates a multi-UAV communication network equipped with edge computing nodes to assist terminal users in task computation. Our goal is to reduce the task processing delay for users through the joint optimization of discrete computation modes, continuous 3D trajectories, and resource assignment. To address the challenges posed by the mixed action space, we propose a Multi-UAV Edge Computing Resource Scheduling (MUECRS) algorithm, which comprises two key components: 1) trajectory optimization, and 2) computation mode and resource management. Experimental results show that our method effectively plans 3D UAV trajectories and enables rapid user coverage. Compared to state-of-the-art baselines, our approach achieves at least 16.5% reduction in task delay, demonstrating superior adaptability and robustness.