On the use machine learning for flexible payload management in VHTS systems

Ortíz Gómez, Flor de Guadalupe and Martínez Rodríguez-Osorio, Ramón and Salas Natera, Miguel Alejandro and Landeros Ayala, Salvador (2019). On the use machine learning for flexible payload management in VHTS systems. In: "70th International Astronautical Congress 2019", 21/10/2019 - 25/10/2019, Washington D. C.. pp. 1-6.

Description

Title: On the use machine learning for flexible payload management in VHTS systems
Author/s:
  • Ortíz Gómez, Flor de Guadalupe
  • Martínez Rodríguez-Osorio, Ramón
  • Salas Natera, Miguel Alejandro
  • Landeros Ayala, Salvador
Item Type: Presentation at Congress or Conference (Article)
Event Title: 70th International Astronautical Congress 2019
Event Dates: 21/10/2019 - 25/10/2019
Event Location: Washington D. C.
Title of Book: Proceedings of 70th International Astronautical Congress 2019
Date: 2019
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Very High Throughput Satellites (VHTS) are next generation of satellite systems to meet the demands of increase on data traffic. The objective of VHTS systems is to achieve 1 Terabit/s by satellite communications in the near future. VHTS systems are based on multi-beam payloads with polarization and frequency reuse schemes, with VHTS using Q/V bands in the feeder link to increase available bandwidth. These systems provide a greater satellite capacity at a reduced cost per Gbps in orbit but further optimization is needed in order to use the full capacity of the satellite over the time. For instance, flexible payloads are required in VHTS to meet changing traffic demands. Whereby, this contribution presents a study of how and where Machine Learning algorithms can be used to manage a flexible payload architecture. The problem of resource allocation in a flexible payload architecture is analyzed to implement the application of ML as a solution for non-uniform traffic demand and its changes over the time in the service area.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2014-55735-C3-1-RENABLING-5GUnspecifiedInnovando en tecnologías radio para redes 5G
Government of SpainTEC2017-85529-C3-1-RFUTURE-RADIOUnspecifiedSistemas y Tecnología Radio para Comunicaciones Terrestres y Espaciales de Gran Capacidad en un Futuro Hiperconectado
Universidad Politécnica de MadridUnspecifiedUnspecifiedUnspecifiedUnspecified

More information

Item ID: 64705
DC Identifier: http://oa.upm.es/64705/
OAI Identifier: oai:oa.upm.es:64705
Official URL: https://www.iafastro.org/assets/files/publications/iac-publications/IAC-2019-General-Programme-online.pdf
Deposited by: Memoria Investigacion
Deposited on: 26 Oct 2020 16:12
Last Modified: 26 Oct 2020 16:12
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