Online Learning for Dynamic Constellation Topologies
This work addresses the challenge of managing satellite network topologies affected by orbital movement and maneuvering, which is incremental as it adapts existing methods to an online setting.
The paper tackles the problem of dynamic network topology configuration for satellite networks under an online learning framework, achieving performance matching state-of-the-art offline methods while demonstrating a trade-off between computational complexity and convergence.
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.