# Online Learning for Dynamic Constellation Topologies

arXiv: https://arxiv.org/abs/2603.25954 · HTML: https://mlsanity.com/paper/2603.25954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2401.09455/) (2023)
- [Time-Dependent Network Topology Optimization for LEO Satellite Constellations](https://mlsanity.com/paper/2501.13280/) (2025)
- [Resilience of Mega-Satellite Constellations: How Node Failures Impact Inter-Satellite Networking Over Time?](https://mlsanity.com/paper/2509.06766/) (2025)

## Built on by (most cited first, 1 of 1)

- [ASTRA: ADMM-Accelerated Topology Reconfiguration for Dynamic Satellite Constellations](https://mlsanity.com/paper/2609.33993/) (2026)

## Abstract (arXiv)

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.