Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866909003737464832 |
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| author | Krishnan, Sivaram Homssi, Bassel Al Gu, Zhouyou Park, Jihong Oh, Sung-Min Choi, Jinho |
| author_facet | Krishnan, Sivaram Homssi, Bassel Al Gu, Zhouyou Park, Jihong Oh, Sung-Min Choi, Jinho |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_27478 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach Krishnan, Sivaram Homssi, Bassel Al Gu, Zhouyou Park, Jihong Oh, Sung-Min Choi, Jinho Machine Learning Systems and Control 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. |
| title | Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2604.27478 |