Name
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, to manage the resources available in flexible payload architectures. A use case is presented to demonstrate the effectiveness of the proposed approach and a discussion is made on all the challenges that are presented. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ASMS/SPSC48805.2020.9268790 | 2020 10th Advanced Satellite Multimedia Systems Conference and the 16th Signal Processing for Space Communications Workshop (ASMS/SPSC) |
Keywords | DocType | ISSN |
VHTS,Satellite Communications,Machine Learning,Flexible Payload,Dynamic Resources Management | Conference | 2329-7093 |
ISBN | Citations | PageRank |
978-1-7281-5795-5 | 0 | 0.34 |
References | Authors | |
2 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Flor G. Ortiz-Gomez | 1 | 0 | 0.34 |
| Daniele Tarchi | 2 | 278 | 39.91 |
| Ramón Martinez | 3 | 4 | 2.41 |
| Alessandro Vanelli-Coralli | 4 | 385 | 49.52 |
| Miguel Alejandro Salas-Natera | 5 | 0 | 0.34 |
| Salvador Landeros-Ayala | 6 | 1 | 0.68 |