Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach

TL;DR

This paper introduces a scalable SDN framework for LEO mega-constellations using graph neural networks and Koopman theory, enabling efficient topology representation and prediction for network management.

Contribution

It presents a novel hierarchical SDN architecture that employs GKAE for spatio-temporal forecasting, improving scalability and accuracy in managing large satellite networks.

Findings

Achieves 42.8% better spatial compression over baselines.

Attains 10.81% improvement in temporal forecasting accuracy.

Uses a smaller model footprint while enhancing control capabilities.

Abstract

Terrestrial network limitations drive the integration of non-terrestrial networks (NTNs), notably mega-constellations comprising thousands of low Earth orbit (LEO) satellites. While these satellites act as interconnected network switches via inter-satellite links (ISLs), their massive scale creates severe bottlenecks for network management. To address this, we propose a scalable, hierarchical software-defined networking (SDN) framework. Our architecture leverages graph neural networks (GNNs) to compactly represent the constellation topology, and Koopman theory to linearize nonlinear dynamics. Specifically, a Graph Koopman Autoencoder (GKAE) forecasts spatio-temporal behavior within a linear subspace for each orbital shell. A central SDN controller then aggregates these shell-level predictions for globally coordinated control. Simulations on the Starlink constellation demonstrate that our approach achieves at least a 42.8\% improvement in spatial compression and a 10.81\% improvement in temporal forecasting compared to established baselines, all while utilizing a significantly smaller model footprint.