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

arXiv: https://arxiv.org/abs/2604.27478 · HTML: https://mlsanity.com/paper/2604.27478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 4 of 4)

- [5G from Space: An Overview of 3GPP Non-Terrestrial Networks](https://mlsanity.com/paper/2103.09156/) (2021)
- [Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6G](https://mlsanity.com/paper/2502.15936/) (2025)
- [Learning Time-Varying Graph Signals via Koopman](https://mlsanity.com/paper/2511.06493/) (2025)
- [Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems](https://mlsanity.com/paper/2601.13662/) (2026)

## Abstract (arXiv)

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.