Reference : Supervised machine learning for power and bandwidth management in very high throughpu...
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Supervised machine learning for power and bandwidth management in very high throughput satellite systems
Ortiz Gomez, Flor de Guadalupe mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Tarchi, Daniele mailto [University of Bologna]
Martinez, Ramon mailto [Universidad Politecnica de Madrid]
Vanelli-Coralli, Alessandro mailto [University of Bologna]
Salas-Natera, Miguel A. mailto [Universidad Politecnica de Madrid]
Landeros, Salvador mailto [Agencia Espacial Mexicana]
International Journal of Satellite Communications and Networking
John Wiley & Sons
[en] dynamic resource management ; Satellite communications ; Machine Learning
[en] In the near future, very high throughput satellite (VHTS) systems are expected to have a high increase in traffic demand. However, this increase will not be uniform over the service area and will be also dynamic. A solution to this problem is given by flexible payload architectures; however, they require that resource management is performed autonomously and with low latency. In this paper, we propose the use of supervised machine learning, in particular a classification algorithm using a neural network, to manage the resources available in flexible payload architectures. Use cases are presented to demonstrate the effectiveness of the proposed approach, and a discussion is made on all the challenges that are presented.
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