Unsupervised learning for cellular power control

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• dc.contributor.author Nikbakht Silab, Rasoul
• dc.contributor.author Jonsson, Anders, 1973-
• dc.contributor.author Lozano Solsona, Angel
• dc.date.accessioned 2021-05-13T09:14:29Z
• dc.date.available 2021-05-13T09:14:29Z
• dc.date.issued 2021
• dc.description.abstract 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.
• dc.description.sponsorship 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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Nikbakht R, Jonsson A, Lozano A. Unsupervised learning for cellular power control. IEEE Commun Lett. 2021;25(3):682-6. DOI: 10.1109/LCOMM.2020.3027994
• dc.identifier.doi http://dx.doi.org/10.1109/LCOMM.2020.3027994
• dc.identifier.issn 1089-7798
• dc.identifier.uri http://hdl.handle.net/10230/47548
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Communications Letters. 2021;25(3):682-6
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/694974
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/RTI2018-102112
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/RTI2018-101040
• dc.rights © 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.2020.3027994
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Machine learning
• dc.subject.keyword Neural networks
• dc.subject.keyword Unsupervised learning
• dc.subject.keyword Power control
• dc.subject.keyword Cellular systems
• dc.title Unsupervised learning for cellular power control
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion