Optimizing 3D Trajectory and Task Offloading in Collaborative UAV-Enabled Mobile Edge Computing Networks

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) networks encounter significant challenges in achieving balanced workload distribution, primarily due to the limited coverage areas of UAVs and their diverse computational capabilities. This paper proposes a UAV-enabled MEC framework that jointly optimizes three-dimensional (3D) trajectory planning and dynamic computation offloading. We formulate a mixed-integer programming (MIP) problem to minimize system latency by simultaneously optimizing UAV trajectory design and task offloading strategies, where UAV mobility and offloading decisions are tightly coupled.Unlike existing approaches that either optimize 3D trajectories without inter-UAV cooperation or implement cooperative computing under fixed altitudes with predetermined relay hops, our framework uniquely integrates adaptive multi-hop collaborative offloading with continuous 3D trajectory planning. The model complexity arises from its hybrid decision structure that simultaneously handles continuous trajectory parameters and discrete offloading variables. Our approach decomposes the problem into two tightly coupled subproblems: (1) 3D UAV trajectory optimization and (2) task offloading scheduling. We then propose a Decoupled Deep Reinforcement Learning for Parallelized Planning and Offloading (DDP3O) algorithm that systematically addresses these interconnected components. Experimental results demonstrate that DDP3O achieves fast convergence and superior performance compared to state-of-the-art methods including block coordinate descent optimization, DQN-based approaches, and fixed-hop cooperative schemes across multiple operational scenarios.