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This research applies online learning frameworks to optimize dynamic configurations in satellite constellations, addressing the challenges posed by continuous orbital movement and maneuvering nodes. The study demonstrates that this approach achieves performance comparable to state-of-the-art offline methods while accommodating constraints without assuming fixed network structures like known orbital planes. By balancing computational complexity per iteration with convergence speed, the work advances the application of machine learning to adaptive space-based network topologies.
Online Learning for Dynamic Constellation Topologies
arXiv:2603.25954v2 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2603.25954 [cs.LG]
(or arXiv:2603.25954v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.25954 Focus to learn more arXiv-issued DOI via DataCite
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