Joint Optimization of Trajectory-Resource Allocation and Deep Task Partial Offloading for MEC-Enabled Multi-UAV

Currently, multiple unmanned aerial vehicles (UAVs) can cooperatively work as mobile edge computing (MEC) servers in the sky to provide computation services to ground terminals (GTs). Such an MEC-enabled multi-UAV system will greatly benefit the GTs, each of which can offload its tasks on demand to a nearby UAV. In particular, if a GT has to process computation-intensive deep learning tasks in a catastrophic environment, it can partially offload these tasks to UAVs using a scheme like Partial Program Offloading (PPO). This ensures the quick processing of the deep learning tasks while saving computing resources on both the GT and UAV sides. Nevertheless, UAV-GT offloading links are frequently blocked by ground obstacles in complicated environments, and individual UAVs may have limited computation capacity. Moreover, UAVs lack a constant propulsion energy supply to sustain a long mission time. All these factors lead to a degraded Quality of Service (QoS) for GTs in terms of task latency. To address this issue, we propose to jointly optimize the UAV trajectories, computing resource allocation, and the partial offloading of deep learning tasks. The formulated joint optimization problem is challenging to solve optimally, as it is non-convex and involves multiple coupled constraints. We propose utilizing the Successive Convex Approximation (SCA) method alongside a Block Coordinate Descent (BCD) approach to tackle this joint problem. Numerical results demonstrate that the proposed joint optimization scheme significantly outperforms the benchmark solutions.