A GCN-GRU Based End-to-End LEO Satellite Network Dynamic Topology Prediction Method

Abstract

Dynamic changes in network topology bring challenges to the management of mega low earth orbit (mega-LEO) systems. End-to-end network topology prediction is one of the key technologies to meet the challenges. At present, the graph theory-based prediction method can predict periodic changing links such as inter-satellite links (ISL) and satellite-ground links (GSL), but it cannot support the prediction of aperiodic user links. Moreover, when the scale of network nodes grows, the memory consumption and calculation time also increase rapidly, and not applicable in LEO mega-constellation networks with more than 10,000 nodes, such as Starlink satellite networks. To address these problems, we propose a prediction method based on graph convolutional neural network (GCN) and gated recursive unit (GRU). The key point of our method is to predict the end-to-end link changes of LEO mega-constellation, while reducing memory consumption and computing time. Simulation results show that the proposed method can achieve the topology prediction accuracy of more than 85% and reduce the memory consumption and computation time by more than 25% and 18.1%, respectively.