让卫星网络动态组网，实时学习最优连接方式。
Online Learning for Dynamic Constellation Topologies
- 基于在线学习框架，实时调整卫星间连接拓扑。
- 性能媲美离线最优方法，无需预设轨道结构。
- 支持资源受限场景，可调计算与收敛速度平衡。
近年来，由于相比纯地面系统具有更高的可用性和覆盖范围，卫星网络的使用显著增加。然而，为有效提供服务，卫星网络必须应对节点持续的轨道运动和机动带来的拓扑变化。本文在在线学习框架下解决动态网络拓扑配置问题。作为副产品，该方法不依赖网络结构假设（如已知轨道平面，可能因机动卫星而被破坏）。实验表明，其问题建模性能可媲美当前最先进的离线方法。更重要的是，该方法适用于约束条件下的在线学习，可在每次迭代的计算复杂度与最终策略收敛之间实现权衡。
原文摘要 · Abstract (English)
The use of satellite networks has increased significantly in recent years due to their advantages over purely terrestrial systems, such as higher availability and coverage. However, to effectively provide these services, satellite networks must cope with the continuous orbital movement and maneuvering of their nodes and the impact on the network's topology. In this work, we address the problem of (dynamic) network topology configuration under the online learning framework. As a byproduct, our approach does not assume structure about the network, such as known orbital planes (that could be violated by maneuvering satellites). We empirically demonstrate that our problem formulation matches the performance of state-of-the-art offline methods. Importantly, we demonstrate that our approach is amenable to constrained online learning, exhibiting a trade-off between computational complexity per iteration and convergence to a final strategy.
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