Memorias de investigación
Communications at congresses:
ON THE USE MACHINE LEARNING FOR FLEXIBLE PAYLOAD MANAGEMENT IN VHTS SYSTEMS
Year:2019
Research Areas
  • Engineering,
  • Telecommunications
Information
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.
International
No
Congress
70TH INTERNATIONAL ASTRONAUTICAL CONGRESS 2019
960
Place
Washington D. C.
Reviewers
Si
ISBN/ISSN
00741795
Start Date
21/10/2019
End Date
25/10/2019
From page
1
To page
6
International Astronautical Federation, IAF
Participants
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Radiación
  • Centro o Instituto I+D+i: Centro de I+d+i en Procesado de la Información y Telecomunicaciones
  • Departamento: Señales, Sistemas y Radiocomunicaciones