Reference : Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/50865
Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems
English
Ortiz Gomez, Flor de Guadalupe mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Lei, Lei mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Lagunas, Eva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Martinez, Ramon mailto [Universidad Politecnica de Madrid > Information Processing and Telecommunications Center]
Tarchi, Daniele mailto [University of Bologna > Department of Electrical, Electronic and Information Engineering]
Querol, Jorge mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Salas, Miguel mailto [Universidad Politecnica de Madrid > Information Processing and Telecommunications Center]
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
16-Mar-2022
Electronics
Multidisciplinary Digital Publishing Institute (MDPI)
11
7
State-of-the-Art in Satellite Communication Networks
992
Yes (verified by ORBilu)
International
2079-9292
Basel
Switzerland
[en] Satellite communications ; Satellite communications ; Machine Learning
[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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/50865
10.3390/electronics11070992
https://www.mdpi.com/2079-9292/11/7/992
FnR ; FNR13696663 > Eva Lagunas > FlexSAT > Resource Optimization For Next Generation Of Flexible Satellite Payloads > 01/03/2020 > 28/02/2023 > 2019

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
electronics-11-00992-v2.pdfPublisher postprint7.33 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.