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Exploring Graph Neural Networks for Joint Cruise Control and Task Offloading in UAV-enabled Mobile Edge Computing
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DOI：10.1109/VTC2023-Spring57618.2023.10200966.png)
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Abstract
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Unmanned aerial vehicles (UAVs) have been increasingly considered as aerial servers in mobile edge computing (MEC) to assist mission-critical computation tasks of edge ground nodes. The tasks are buffered at the ground node, while the task offloading is scheduled by the UAV. When one ground node in MEC is scheduled to offload its tasks, other unselected ground nodes' tasks could expire and be cancelled. To maximize the offloaded tasks to the UAV, this paper proposes a new joint optimization of cruise control and task offloading scheduling, which synthetically takes into account the computation capacity and battery energy of the ground nodes, and the speed limit of the UAV. Given a large and unknown network state and action space, a new deep reinforcement learning (DRL) framework based on graph neural networks (GNN) is developed to train online the continuous cruise control of the UAV and the task offloading schedule. Particularly, GNN explores feature correlations of network states to supervise the action training of the UAV in DRL. We implement the proposed GNN-DRL framework on Google Tensorflow. Extensive numerical results show that GNN-DRL improves the task offloading rate by 43%, compared to the DRL solution without GNN.
Keywords:
Unmanned aerial vehicle
Mobile edge computing
Graph neural network
Deep reinforcement learning
Task offloading rate
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