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Título del libro: 2020 Ieee 11th Latin American Symposium On Circuits And Systems, Lascas 2020
Título del capítulo: Applying Learning Methods to Optimize the Ground Segment for HTS Systems

Autores UNAM:
SALVADOR LANDEROS AYALA; JOSE MARIA MATIAS MARURI;
Autores externos:

Idioma:

Año de publicación:
2020
Palabras clave:

Gateways (computer networks); Rain; Satellite links; Ground segments; High availability; Learning methods; Network scheme; On-machines; Optimal number; Rain attenuation; System modeling; Learning systems


Resumen:

The rain attenuation affects the availability of the Ground Segment network, impacting mainly to the satellite links. The gateway station feeder uplink works in Q/V band, which turns into very susceptible to weather impairments. This means that it is inactive when the feeder link is affected by a rain fading event. Therefore, we propose learning methods to improve a network scheme being capable of ensuring high availability and reducing the number of redundant gateways on the ground segment. These methods are based on machine learning techniques that are adequately implemented in our system model. Finally, we evaluate the performance of the system model to demonstrate that our implementation provides high availability and an optimal number of redundant gateways. © 2020 IEEE.


Entidades citadas de la UNAM: