Ensemble extreme learning machine based ...
Document type :
Communication dans un congrès avec actes
Title :
Ensemble extreme learning machine based equalizers for OFDM systems
Author(s) :
Saideh, Michel [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Simon, Eric [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Farah, Joumana [Auteur]
Villain, Jonathan [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [COSYS-LEOST ]
Fleury, Anthony [Auteur]
Deniau, Virginie [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [COSYS-LEOST ]
Gransart, Christophe [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [COSYS-LEOST ]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Simon, Eric [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Farah, Joumana [Auteur]
Villain, Jonathan [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [COSYS-LEOST ]
Fleury, Anthony [Auteur]
Deniau, Virginie [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [COSYS-LEOST ]
Gransart, Christophe [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [COSYS-LEOST ]
Conference title :
2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS)
City :
Adelaide (en ligne)
Country :
Australie
Start date of the conference :
2020-12-14
Book title :
14th International Conference on Signal Processing and Communication Systems (ICSPCS 2020)
Journal title :
Proceedings of the 14th International Conference on Signal Processing and Communication Systems, ICSPCS 2020
English keyword(s) :
Extreme Learning Machine
Ensemble Learning
OFDM
Equalization
Ensemble Learning
OFDM
Equalization
HAL domain(s) :
Sciences de l'ingénieur [physics]
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
English abstract : [en]
Extreme Learning Machine (ELM) technology has started gaining interest in the channel estimation and equalization aspects of wireless communications systems. This is due to its fast training and global optimization ...
Show more >Extreme Learning Machine (ELM) technology has started gaining interest in the channel estimation and equalization aspects of wireless communications systems. This is due to its fast training and global optimization capabilities that might allow the ELM-based receivers to be deployed in an online mode while facing the channel scenario at hand. However, ELM still needs a relatively large amount of training samples, thus causing important losses in spectral resources. In this work, we make use of the ensemble learning theory to propose different ensemble learning-based ELM equalizers that need much less spectral resources, while achieving better performance accuracy. Also, we verify the robustness of our proposed equalizers within different communication settings and channel scenarios by conducting different Monte Carlo simulations.Show less >
Show more >Extreme Learning Machine (ELM) technology has started gaining interest in the channel estimation and equalization aspects of wireless communications systems. This is due to its fast training and global optimization capabilities that might allow the ELM-based receivers to be deployed in an online mode while facing the channel scenario at hand. However, ELM still needs a relatively large amount of training samples, thus causing important losses in spectral resources. In this work, we make use of the ensemble learning theory to propose different ensemble learning-based ELM equalizers that need much less spectral resources, while achieving better performance accuracy. Also, we verify the robustness of our proposed equalizers within different communication settings and channel scenarios by conducting different Monte Carlo simulations.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Comment :
ISBN 978-1-7281-9973-3 ; e-ISBN 978-1-7281-9971-9
Source :
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