Supervised machine learning for power and bandwidth management in very high throughput satellite systems-Reference-Cited by-同舟云学术

Supervised machine learning for power and bandwidth management in very high throughput satellite systems

Published:2021-08-22 Issue: Volume: Page:
ISSN:1542-0973
Container-title:International Journal of Satellite Communications and Networking
language:en
Short-container-title:Int J Satell Commun Network

Author:

Ortiz‐Gómez Flor G.¹^ORCID,Tarchi Daniele²^ORCID,Martínez Ramón¹^ORCID,Vanelli‐Coralli Alessandro²^ORCID,Salas‐Natera Miguel A.¹^ORCID,Landeros‐Ayala Salvador³⁴^ORCID

Affiliation:

1. Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain

2. Department of Electrical, Electronic and Information Engineering University of Bologna Bologna Italy

3. Agencia Espacial Mexicana Mexico City Mexico

4. Faculty of Engineering Universidad Nacional Autónoma de México Mexico City Mexico

Funder

Universidad Politécnica de Madrid

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Media Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sat.1422

Reference27 articles.

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2. Correction: Ortiz‐Gomez, F.G. et al. Forward link optimization for the design of VHTS satellite networks. Electronics 2020, 9, 473;Ortiz‐Gomez FG;Electronics,2020

3. AG‐DPA: Assignment game‐based dynamic power allocation in multibeam satellite systems;Liu S;Int J Satell Commun Netw,2020

4. Ortíz‐GómezFG Martínez Rodríguez‐OsorioR Salas‐NateraM Landeros‐AyalaS.Adaptive resources allocation for flexible payload enabling VHTS systems: methodology and architecture. In:36th International Satellite Communications Systems Conference (ICSSC2018) Institution of Engineering and Technology.Niagara Falls ON Canada;2018:1‐8.

5. Radio resource management optimization of flexible satellite payloads for DVB‐S2 systems;Cocco G;IEEE Trans Broadcast,2018

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