GRouting: Dynamic Routing for LEO Satellite Networks with Graph-based Deep Reinforcement Learning
Hao Wang, Yongyi Ran, Lei Zhao, Junxia Wang, Jiangtao Luo, Tao Zhang
Abstract
The effective and reliable routing for Low Earth Orbit (LEO) satellite networks is intractable. The existing approaches cannot well handle the time-varying topology, frequent link handover, and imbalanced communication load. To tackle these issues, in this paper, we propose GRouting algorithm combining Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to dynamically find the optimal routing paths for LEO satellite networks. First, GNN is employed to learn the representation of satellite networks with non-Euclidean data structures. GNN is able to generalize over arbitrary satellite networks topologies, which means that it can deal with time-varying states of satellite networks. Then, based on the representation learned by GNN, DRL is applied to select the optimal routing path between two satellites, which can maximize the utilization of network resources while guaranteeing the requirement of transmission delay. Finally, extensive simulation experiments are carried out to illustrate that 1) our method has a better performance than the baseline algorithms, and 2) the GNN-based method can achieve better generalization over time-varying topologies.