Causality-Aware Scheduling and Mobility Control in Multi-UAV Cooperative MEC via Graph-Enhanced Dual-Timescale Learning
Multi-uncrewed aerial vehicle (UAV) cooperative mobile edge computing (MEC) systems present significant challenges owing to task causal dependencies, dynamic channel variations, and multi-dimensional resource coupling. In this study, a multi-UAV cooperative MEC system with task causality constraints and time-varying wireless channels is considered, and the joint optimization of task offloading, task migration, dynamic UAV clustering, and continuous UAV trajectory planning is investigated. The objective is to minimize the long-term weighted sum of system latency and energy consumption while ensuring task queue stability. Lyapunov optimization is first introduced to transform the formulated stochastic mixed-integer problem into a deterministic per-slot optimization. Afterward, a dual-timescale graph-enhanced multi-agent proximal policy optimization (DT-HGMAPPO) framework is proposed to coordinate long-timescale UAV clustering and trajectory planning with short-timescale task offloading and migration. Specifically, this framework decouples the problem by utilizing dual-layer weighted hypergraph matching (DL-WHM) for joint clustering and association, a dynamic priority scoring (DPS) mechanism for intra-cluster load balancing, and a graph-enhanced MAPPO algorithm for trajectory optimization. Simulation results reveal that the proposed DT-HGMAPPO algorithm outperforms conventional multi-agent deep reinforcement learning baselines in terms of both convergence speed and policy stability. It achieves a final reward that is at least 18.0% greater than that of other multi-agent algorithms. Moreover, the proposed framework reduces total system cost by 33.3% compared with MASAC and 18.9% compared with MATD3, thereby achieving a superior delay-energy trade-off while ensuring queue stability.