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Article

Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems

1
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 4365 Luxembourg, Luxembourg
2
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3
Information Processing and Telecommunications Center, Universidad Politecnica de Madrid, 28040 Madrid, Spain
4
Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Mauro Tropea
Electronics 2022, 11(7), 992; https://doi.org/10.3390/electronics11070992
Received: 28 January 2022 / Revised: 16 March 2022 / Accepted: 16 March 2022 / Published: 23 March 2022
(This article belongs to the Special Issue State-of-the-Art in Satellite Communication Networks)
Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and changes in traffic demand diurnal. This problem is addressed by using flexible payload architectures, which allow payload resources to be flexibly allocated to meet the traffic demand of each beam. While optimization-based radio resource management (RRM) has shown significant performance gains, its intense computational complexity limits its practical implementation in real systems. In this paper, we discuss the architecture, implementation and applications of Machine Learning (ML) for resource management in multibeam GEO satellite systems. We mainly focus on two systems, one with power, bandwidth, and/or beamwidth flexibility, and the second with time flexibility, i.e., beam hopping. We analyze and compare different ML techniques that have been proposed for these architectures, emphasizing the use of Supervised Learning (SL) and Reinforcement Learning (RL). To this end, we define whether training should be conducted online or offline based on the characteristics and requirements of each proposed ML technique and discuss the most appropriate system architecture and the advantages and disadvantages of each approach. View Full-Text
Keywords: satellite communications; radio resource management; flexible payload; beam hopping; machine learning; supervised learning; reinforcement learning satellite communications; radio resource management; flexible payload; beam hopping; machine learning; supervised learning; reinforcement learning
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MDPI and ACS Style

Ortiz-Gomez, F.G.; Lei, L.; Lagunas, E.; Martinez, R.; Tarchi, D.; Querol, J.; Salas-Natera, M.A.; Chatzinotas, S. Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems. Electronics 2022, 11, 992. https://doi.org/10.3390/electronics11070992

AMA Style

Ortiz-Gomez FG, Lei L, Lagunas E, Martinez R, Tarchi D, Querol J, Salas-Natera MA, Chatzinotas S. Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems. Electronics. 2022; 11(7):992. https://doi.org/10.3390/electronics11070992

Chicago/Turabian Style

Ortiz-Gomez, Flor G., Lei Lei, Eva Lagunas, Ramon Martinez, Daniele Tarchi, Jorge Querol, Miguel A. Salas-Natera, and Symeon Chatzinotas. 2022. "Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems" Electronics 11, no. 7: 992. https://doi.org/10.3390/electronics11070992

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