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Article (Scientific journals)
Convolutional Neural Networks for Flexible Payload Management in VHTS Systems
Ortiz Gomez, Flor de Guadalupe; Tarchi, Daniele; Martinez, Ramon et al.
2021In IEEE Systems Journal, 15 (3), p. 4675 - 4686
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The original publication is available at https://ieeexplore.ieee.org/document/9193896

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Keywords :
dynamic resource management; Satellite communications; Machine Learning
Abstract :
[en] Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the nonuniform user distribution, and the changing traffic demand during the day. This problem is addressed using flexible payload architectures, enabling the allocation of the payload resources in a flexible manner to meet traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to the VHTS systems, which is why in this article, we are analyzing the use of convolutional neural networks (CNNs) to manage the resources available in flexible payload architectures for DRM. The VHTS system model is first outlined, for introducing the DRM problem statement and the CNN-based solution. A comparison between different payload architectures is performed in terms of DRM response, and the CNN algorithm performance is compared by three other algorithms, previously suggested in the literature to demonstrate the effectiveness of the suggested approach and to examine all the challenges involved.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Ortiz Gomez, Flor de Guadalupe ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Tarchi, Daniele;  University of Bologna
Martinez, Ramon;  Universidad Politecnica de Madrid
Vanelli-Coralli, Alessandro;  University of Bologna
Salas-Natera, Miguel A.;  Universidad Politecnica de Madrid
Landeros, Salvador;  Agencia Espacial Mexicana
External co-authors :
Language :
Title :
Convolutional Neural Networks for Flexible Payload Management in VHTS Systems
Publication date :
September 2021
Journal title :
IEEE Systems Journal
Publisher :
Institute of Electrical and Electronics Engineers, United States - New York
Volume :
Issue :
Pages :
4675 - 4686
Peer reviewed :
Peer Reviewed verified by ORBi


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