← Back to all news
arXiv Machine Learning By Jo\~ao Norberto, Ricardo Ferreira, Cl\'audia Soares

Online Learning for Dynamic Constellation Topologies

Read the original on arXiv Machine Learning →

The paper proposes an online learning framework for configuring dynamic constellation topologies in satellite networks, addressing the challenges posed by continuous orbital movement and node maneuvering. It does not rely on predefined orbital plane structures, making it robust to changes caused by satellite maneuvers. Experiments show that the method performs comparably to state‑of‑the‑art offline techniques and can be adapted to constrained online learning, balancing per‑iteration computational cost against convergence speed.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
5d ago

Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks

The paper introduces TELGEN, a traffic engineering algorithm that uses graph neural networks to predict an optimal TE algorithm rather than a direct solution. TELGEN generalizes across diverse network topologies and traffic patterns, achieving less than a 3% optimality gap on networks up to 5,000 nodes and 3.6 million links, while reducing solving time by up to 84% and training time by up to 79.6% compared to existing methods.

By Fangtong Zhou, Xiaorui Liu, Ruozhou Yu, Guoliang Xue
benchmarks
More like this →
arXiv Machine Learning
Aug 5

FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations

arXiv:2608. 03436v1 Announce Type: cross Abstract: Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or synchronized training inefficient and hard to scale.

By Ziwu Liu, In\^es Pinto Gouveia, Rehana Yasmin, Paulo Esteves-Verissimo, Ali Shoker
More like this →
arXiv AI
Sep 2

FractalNet-Based Heterogeneous Federated Learning for Orbital Edge Intelligence in Satellite Mega-Constellations: A Wildfire Case Study

The paper introduces a heterogeneous federated learning approach using the FractalNet architecture tailored for satellite mega‑constellations. It formalizes contact‑window‑constrained, depth‑heterogeneous optimization and proposes a distributed path scheduler that assigns model depth based on satellite SWAP‑C constraints, predicted contacts, and training statistics. The framework includes periodic update pooling and a three‑tier agentic control plane, and is validated through a wildfire detection case study across LEO, MEO, and GEO/HEO shells, demonstrating improvements in convergence, communication efficiency, energy adaptation, and robustness.

By Sai Puppala, Koushik Sinha
agents
More like this →
arXiv AI
Jul 29

Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling

arXiv:2607. 25835v1 Announce Type: new Abstract: Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically.

By Itai Zilberstein, Pranav Rajbhandari, Steve Chien, Tuomas Sandholm
benchmarks
More like this →