QoS-oriented Task Offloading in NOMA-based Multi-UAV Cooperative MEC Systems

As resource-intensive and latency-sensitive applications continue to expand, the integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) has emerged as a viable solution, offering flexible, on-demand services for mobile users (MUs) without reliance on terrestrial infrastructure. The adoption of non-orthogonal multiple access (NOMA) further reduces latency by allowing MUs to offload tasks simultaneously over a single subchannel. However, many existing offloading methods do not explicitly incorporate a priority-based task scheduling mechanism and instead optimize task execution based on system constraints such as latency or energy consumption. To bridge this gap, we propose a QoS-oriented task offloading scheme that systematically optimizes task scheduling. We formulate an average system utility maximization problem that jointly optimizes UAVs’ 3D trajectories, MU association, task offloading ratios, and resource allocation. The optimization problem is inherently complex due to its non-convex nature and multiple constraints. To address this, we first employ Lagrange duality to decouple constraints, reducing computational complexity. Subsequently, we propose a novel improved soft actor-critic (ISAC) algorithm, which incorporates a perturbation term into the loss function to guide the training process away from local minima and toward globally optimal solutions. Through extensive simulation, we demonstrate that the ISAC algorithm guarantees convergence and significantly outperforms benchmark methods on offloading transmission rates, task completion rates, and overall system utility.