Scheduling of graph neural network and Markov based UAV mobile edge computing networks

Abstract

With the rapid advancement of communication technology, unmanned aerial vehicles (UAVs) have gained significant attention. The incorporation of UAVs in mobile edge computing (MEC) systems has proven to be a viable method for improving system throughput and decreasing computing latency. In pursuit of enhancing the quality of service (QoS) for users, we propose a UAV-assisted MEC system. Our approach entails the utilization of a multi-agent graph convolutional deep reinforcement learning algorithm that facilitates the transmission of distributed information and computing tasks from multiple ground users to the UAV. Through individual strategy training, users are able to effectively learn collaborative strategies. Simulation results validate the efficacy of the proposed UAV-assisted MEC scheduling method, which is based on graph neural networks (GNN), in significantly enhancing system throughput.