Multi-UAV Collaborative Edge Computing Algorithm for Joint Task Offloading and Channel Resource Allocation

Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS) is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR) of a period time. In particular, the simulated annealing (SA) algorithm with random perturbations is used for optimal channel allocation, aiming to reduce interference and minimize transmission delay. A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile, when jointly considering computation and channel resources, the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.