用图神经网络和动力学理论，让上万颗低轨卫星组网更高效。
Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
- 用图神经网络压缩卫星拓扑，结合科波曼理论线性化动态行为
- 在星链仿真中实现空间压缩提升42.8%，时间预测精度提高10.81%
- 适合大规模低轨星座网络管理研究者与系统设计人员
地面网络局限推动非地面网络（NTNs）的发展，特别是由数千颗低地球轨道（LEO）卫星组成的巨型星座。这些卫星通过星间链路互连，充当网络交换机，但其庞大规模带来了严重的网络管理瓶颈。为此，我们提出一种可扩展的分层软件定义网络（SDN）框架。该架构利用图神经网络（GNNs）紧凑表示星座拓扑，并采用科波曼理论线性化非线性动态。具体地，图科波曼自编码器（GKAE）在每个轨道层内于线性子空间中预测时空行为。中央SDN控制器则聚合各层预测结果，实现全局协同控制。在星链星座的仿真中，本方法相较现有基线实现至少42.8%的空间压缩提升和10.81%的时间预测精度改善，同时模型体积显著减小。
原文摘要 · Abstract (English)
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.
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