ON THE USE OF NEURAL NETWORKS FOR FLEXIBLE PAYLOAD MANAGEMENT IN VHTS SYSTEMS
Por:
Ortíz-Gómez F.G., Rodríguez-Osorio R.M., Salas-Natera M.A., Landeros-Ayala S., Tarchi D., Vanelli-Coralli A.
Publicada:
1 ene 2019
Resumen:
Very High Throughput Satellites (VHTS) surpass the capacity of traditional systems providing FSS and BSS (fixed and broadcasting satellite services, respectively) using multi-beam coverage. The objective of VHTS systems is to achieve a satellite capacity of 1 Terabit/s in the near future. These systems provide greater satellite capacity at a reduced cost per Gbps in orbit, but further optimization is needed to use the full capacity of the satellite over time as traffic demand is non-uniform and changing over time. In other words, VHTS systems require flexible payloads to meet changing traffic demands. This paper presents a solution for the automatic management of a flexible payload architecture using a Neural Network and considering resource allocation as a classification problem. © 2019 FGM Events LLC. All rights reserved.
Filiaciones:
Ortíz-Gómez F.G.:
ETSI Telecomunicación. Information, Processing and Telecommunications Center, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid, 28040, Spain
Rodríguez-Osorio R.M.:
ETSI Telecomunicación. Information, Processing and Telecommunications Center, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid, 28040, Spain
Salas-Natera M.A.:
ETSI Telecomunicación. Information, Processing and Telecommunications Center, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid, 28040, Spain
Landeros-Ayala S.:
Universidad Nacional Autónoma de México, C.U., 04360, Mexico City, 04510, Mexico
Tarchi D.:
Department of Electrical, Electronic and Information Engineering, University of Bologna, BO, Bologna, 40126, Italy
Vanelli-Coralli A.:
Department of Electrical, Electronic and Information Engineering, University of Bologna, BO, Bologna, 40126, Italy
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