Joint Optimization of Flying Trajectory and Task Offloading for UAV-enabled MEC Networks: A Digital Twin-Assisted Hybrid Learning Approach

Unmanned Aerial Vehicles (UAVs), with their high levels of flexibility and maneuverability, can greatly enhance the capabilities of Mobile Edge Computing (MEC) by acting as edge computing servers. In practice, however, it is often challenging to jointly optimize the flying trajectories of UAVs and the offloading decisions of tasks, due to the fast and randomly changing of physical environments. In this work, we investigate an UAV-enable MEC network with the assistance of Digital Twin (DT), where a DT layer is introduced to simulate the Physical Entity (PE) layer, generate different strategies, and evaluate their performances. Specifically, we formulate a joint flying trajectories, task offloading, and resource allocation problem on the DT layer, aiming at minimizing both task delay and energy consumption, under the maximum tolerated delay and resource constraints. To solve the problem in an online distributed manner and implement the derived strategies on the real PE layer, we propose a hierarchical learning approach, which consists of a Deep Reinforcement Learning (DRL) module and a Constrained Optimization (CO) module. First, the DRL module determines the UAVs' flying trajectories. Then, the CO module determines the MDs' task offloading decisions and the associated resource allocations, given the UAVs' flying decisions. Finally, the outputs of both modules are combined together to train the DRL module by using the Deep Deterministic Policy Gradient (DDPG) method. Experiment results show that our proposed DT-assisted scheme outperforms existing benchmark schemes in terms of both task delay and energy cost.