Distributed Deep Learning-based Task Offloading for UAV-enabled Mobile Edge Computing

Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) is considered to offer computational capabilities to the resource-constraints end-users. In this paper, we study the task offloading strategy in UAV-enabled MEC systems, where end-users offload the computation-intensive tasks to the UAV to minimize the overall cost in terms of the weighted delay and energy consumption. The end-users either process the task by itself or offload the tasks to the UAV that acts as a computing access point. However, due to the computation bottleneck and limited channel capacity between UAV and the end-users, it becomes a challenging issue to offload the entire tasks to the UAV. Thus, to find the optimal offloading decision for the tasks generated by the end-users, we build a distributed deep neural network (DNN). In the proposed distributed DNN model, we train multiple DNNs in the same training instance, and finally, for validation, we select the DNN that gives the least training loss. For faster convergence of the training process, we use the optimal generated offloading decision using a Quadratically Constrained Linear Program (QCLP) with Semidefinite Relaxation (SDR). The extensive simulation results show that the offloading decision produced by the trained DNN can achieve near-optimal performance with numerous system parameter settings.