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.

Ortiz‐Gómez, F.G., Tarchi, D., Martínez, R., Vanelli‐Coralli, A., Salas‐Natera, M.A., Landeros‐Ayala, S. (2022). Supervised machine learning for power and bandwidth management in very high throughput satellite systems. INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING, 40(6), 392-407 [10.1002/sat.1422].

Supervised machine learning for power and bandwidth management in very high throughput satellite systems

Tarchi, Daniele;Vanelli‐Coralli, Alessandro;
2022

Abstract

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.
2022
Ortiz‐Gómez, F.G., Tarchi, D., Martínez, R., Vanelli‐Coralli, A., Salas‐Natera, M.A., Landeros‐Ayala, S. (2022). Supervised machine learning for power and bandwidth management in very high throughput satellite systems. INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING, 40(6), 392-407 [10.1002/sat.1422].
Ortiz‐Gómez, Flor G.; Tarchi, Daniele; Martínez, Ramón; Vanelli‐Coralli, Alessandro; Salas‐Natera, Miguel A.; Landeros‐Ayala, Salvador...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/830241
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