Unsupervised learning for cellular power control

This letter applies a feedforward neural network trained in an unsupervised fashion to the problem of optimizing the transmit powers in cellular wireless systems. Both uplink and downlink are considered, with either centralized or distributed power control. Various objectives are entertained, all of...

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Detalles Bibliográficos
Autores: Nikbakht Silab, Rasoul, Jonsson, Anders, 1973-, Lozano Solsona, Angel
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/47548
Acceso en línea:http://hdl.handle.net/10230/47548
http://dx.doi.org/10.1109/LCOMM.2020.3027994
Access Level:acceso abierto
Palabra clave:Machine learning
Neural networks
Unsupervised learning
Power control
Cellular systems
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spelling Unsupervised learning for cellular power controlNikbakht Silab, RasoulJonsson, Anders, 1973-Lozano Solsona, AngelMachine learningNeural networksUnsupervised learningPower controlCellular systemsThis letter applies a feedforward neural network trained in an unsupervised fashion to the problem of optimizing the transmit powers in cellular wireless systems. Both uplink and downlink are considered, with either centralized or distributed power control. Various objectives are entertained, all of them such that the problem can be cast in convex form. The performance of the proposed procedure is very satisfactory and, in terms of computational cost, the scalability with the system dimensionality is markedly superior to that of convex solvers. Moreover, the optimization relies on directly measurable channel gains, with no need for user location information.This work was supported by the European Research Council under the H2020 Framework Programme/ERC grant agreement 694974, by the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) as well as by MINECO’s Projects RTI2018-102112 and RTI2018-101040, and by the ICREA Academia Program.Institute of Electrical and Electronics Engineers (IEEE)202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/47548http://dx.doi.org/10.1109/LCOMM.2020.3027994reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésIEEE Communications Letters. 2021;25(3):682-6info:eu-repo/grantAgreement/EC/H2020/694974info:eu-repo/grantAgreement/ES/2PE/RTI2018-102112info:eu-repo/grantAgreement/ES/2PE/RTI2018-101040© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/LCOMM.2020.3027994info:eu-repo/semantics/openAccessoai:recercat.cat:10230/475482026-05-29T05:05:01Z
dc.title.none.fl_str_mv Unsupervised learning for cellular power control
title Unsupervised learning for cellular power control
spellingShingle Unsupervised learning for cellular power control
Nikbakht Silab, Rasoul
Machine learning
Neural networks
Unsupervised learning
Power control
Cellular systems
title_short Unsupervised learning for cellular power control
title_full Unsupervised learning for cellular power control
title_fullStr Unsupervised learning for cellular power control
title_full_unstemmed Unsupervised learning for cellular power control
title_sort Unsupervised learning for cellular power control
dc.creator.none.fl_str_mv Nikbakht Silab, Rasoul
Jonsson, Anders, 1973-
Lozano Solsona, Angel
author Nikbakht Silab, Rasoul
author_facet Nikbakht Silab, Rasoul
Jonsson, Anders, 1973-
Lozano Solsona, Angel
author_role author
author2 Jonsson, Anders, 1973-
Lozano Solsona, Angel
author2_role author
author
dc.subject.none.fl_str_mv Machine learning
Neural networks
Unsupervised learning
Power control
Cellular systems
topic Machine learning
Neural networks
Unsupervised learning
Power control
Cellular systems
description This letter applies a feedforward neural network trained in an unsupervised fashion to the problem of optimizing the transmit powers in cellular wireless systems. Both uplink and downlink are considered, with either centralized or distributed power control. Various objectives are entertained, all of them such that the problem can be cast in convex form. The performance of the proposed procedure is very satisfactory and, in terms of computational cost, the scalability with the system dimensionality is markedly superior to that of convex solvers. Moreover, the optimization relies on directly measurable channel gains, with no need for user location information.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/47548
http://dx.doi.org/10.1109/LCOMM.2020.3027994
url http://hdl.handle.net/10230/47548
http://dx.doi.org/10.1109/LCOMM.2020.3027994
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Communications Letters. 2021;25(3):682-6
info:eu-repo/grantAgreement/EC/H2020/694974
info:eu-repo/grantAgreement/ES/2PE/RTI2018-102112
info:eu-repo/grantAgreement/ES/2PE/RTI2018-101040
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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