Publications Conference paper · Mega-constellations · 2026

Online Learning for Predictive Satellite Telemetry Analysis: Towards a Digital Twin for Constellation Management

Arnau Dolz-Puig, Adrian Perez-Portero, Juan A. Fraire, and Anna Calveras

Joint European Conference on Networks and Communications and 6G Summit (EuCNC/6G Summit), pp. 60–67, 2026

Abstract

The deployment of Low Earth Orbit (LEO) satellite constellations is transforming global connectivity, yet their management remains challenging due to dynamic topologies and intermittent connectivity inherent in Non Terrestrial Networks (NTNs). Traditional offline approaches fail to capture realtime operational changes, and batch Machine Learning (ML) methods cannot adapt to the continuous, evolving nature of satellite telemetry. To address this, we propose and validate an Online Machine Learning (OML) framework for predictive telemetry analysis, designed as the analytics engine of a broader Digital Twin (DT) architecture that integrates the Distributed Satellite Simulator (DSS-SIM) for orbital and network modeling. We develop a comprehensive statistical methodology that combines Principal Component Analysis (PCA) based clustering, Granger causality, and change-point detection to identify optimal predictor variables from high-dimensional telemetry streams. Validated on real 6GStarLab (6GSL) mission data, the framework identifies Attitude and Determination Control System (ADCS) variables as precursors to battery voltage changes, achieving an 82 -minute early warning before a confirmed event. Deployed inside the DT, this far exceeds the typical fewminute LEO ground-contact window and contrasts with reactive approaches that detect anomalies only after the event. These results demonstrate the operational viability of online learning for real-time satellite state prediction, providing a foundation for proactive NTN resource management in sparse LEO constellations.

Cite

DOI 10.1109/eucnc/6gsummit68295.2026.11577319

BibTeX

@inproceedings{dolz2026onlinelearning,
  author    = {Dolz-Puig, Arnau and Perez-Portero, Adrian and Fraire, Juan A. and Calveras, Anna},
  title     = {Online Learning for Predictive Satellite Telemetry Analysis: Towards a Digital Twin for Constellation Management},
  booktitle = {Joint European Conference on Networks and Communications and 6G Summit (EuCNC/6G Summit)},
  pages     = {60--67},
  year      = {2026},
  doi       = {10.1109/eucnc/6gsummit68295.2026.11577319}
}

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