Fairness-Aware Joint Optimization of 3D Trajectory and Task Offloading in Multi-UAV Edge Computing Systems

Unmanned Aerial Vehicles (UAVs) integrated with Mobile Edge Computing (MEC) servers are deployed to achieve rapid service coverage and deliver essential computational resources to end users. However, existing multi-UAV assisted MEC systems face challenges in balancing high-quality service with fairness. This paper investigates a fairness-based 3D trajectory optimization and task offloading strategy in multi-UAV assisted MEC, introducing a two-layer UAV model (fixed wing and rotary wing) to jointly optimize offloading strategies and flight trajectories. A Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm is developed to solve the inherently non-convex optimization problem in high-dimensional action spaces. Comparative results show up to 16 % reduction in system delay compared to MATD3-FH.