Article (Scientific journals)
Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems
Ortiz Gomez, Flor de Guadalupe; Lei, Lei; Lagunas, Eva et al.
2022In Electronics, 11 (7), p. 992
Peer reviewed
 

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Keywords :
Satellite communications; Machine Learning
Abstract :
[en] 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.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Disciplines :
Electrical & electronics engineering
Author, co-author :
Ortiz Gomez, Flor de Guadalupe  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Lei, Lei ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Lagunas, Eva  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Martinez, Ramon;  Universidad Politecnica de Madrid > Information Processing and Telecommunications Center
Tarchi, Daniele;  University of Bologna > Department of Electrical, Electronic and Information Engineering
Querol, Jorge  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Salas, Miguel;  Universidad Politecnica de Madrid > Information Processing and Telecommunications Center
Chatzinotas, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
yes
Language :
English
Title :
Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems
Publication date :
16 March 2022
Journal title :
Electronics
eISSN :
2079-9292
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI), Basel, Switzerland
Special issue title :
State-of-the-Art in Satellite Communication Networks
Volume :
11
Issue :
7
Pages :
992
Peer reviewed :
Peer reviewed
FnR Project :
FNR13696663 - Resource Optimization For Next Generation Of Flexible Satellite Payloads, 2019 (01/03/2020-31/08/2023) - Eva Lagunas
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 26 April 2022

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