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.
Supervised machine learning for power and bandwidth management in very high throughput satellite systems / Ortiz‐Gómez, Flor G.; Tarchi, Daniele; Martínez, Ramón; Vanelli‐Coralli, Alessandro; Salas‐Natera, Miguel A.; Landeros‐Ayala, Salvador. - In: INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING. - ISSN 1542-0973. - ELETTRONICO. - 40:6(2022), pp. 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.File | Dimensione | Formato | |
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SupervisedLearning_VHTS (6).pdf
Open Access dal 23/08/2022
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