Machine Learning for Satellite Communications Operations
Por:
Vazquez, MA, Henarejos, P, Pappalardo, I, Grechi, E, Fort, J, Gil, JC, Lancellotti, RM
Publicada:
1 ene 2021
Resumen:
This article introduces the application of machine learning (ML)-based procedures in real-world satellite communication operations. While the application of ML in image processing has led to unprecedented advantages in new services and products, the application of ML in wireless systems is still in its infancy. In particular, this article focuses on the introduction of ML-based mechanisms in satellite network operation centers such as interference detection, flexible payload configuration, and congestion prediction. Three different use cases are described, and the proposed ML models are introduced. All the models have been constructed using real data and considering current operations. As reported in the numerical results, the proposed ML-based techniques show good numerical performance: The interference detector presents a false detection probability decrease of 44 percent, the flexible payload optimizer reduces the unmet capacity by 32 percent, and the traffic predictor reduces the prediction error by 10 percent compared to other approaches. In light of these results, the proposed techniques are useful in the process of automating satellite communication systems. © 1979-2012 IEEE.
Filiaciones:
Vazquez, MA:
Ctr Tecnol Telecomunicac Catalunya, Castelldefels, Spain
Henarejos, P:
Ctr Tecnol Telecomunicac Catalunya, Castelldefels, Spain
Pappalardo, I:
Data Reply, London, England
Grechi, E:
Eutelsat, Serv Operat, Paris, France
Fort, J:
European Ctr Space Applicat & Telecommun, Harwell, Berks, England
Gil, JC:
GMV Aerosp Isaac Newton, Tres Cantos, Spain
Lancellotti, RM:
Data Reply, London, England
Green Submitted, All Open Access; Green Open Access
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