Routing in Scalable Delay-Tolerant Space Networks with Graph Neural Networks.

Space networks are challenged by long propagation delays and prolonged link disruptions, which require DelayTolerant Networking (DTN) mechanisms. Contact Graph Routing (CGR) exploits the a priori available topology knowledge derived from the predictable trajectories to compute optimal end-to-end routes. However, state-of-the-art CGR is limited in the scalability and stability of the computation effort, which is critical in resource-constrained spacecraft. To overcome this issue, this paper presents GAUSS: a graph neural network-based routing for scalable delaytolerant space networks. By harvesting recent advances in Graph Neural Networks (GNNs), we can improve the scalability of CGR by a factor of two while narrowing the variability down to one-third in realistic cislunar and near-Earth systems.