Unsupervised learning for C-RAN power control and power allocation

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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 2021-05-13T09:14:25Z
• dc.date.available 2021-05-13T09:14:25Z
• 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 centralized radio access networks operating on a cell-free basis. Both uplink and downlink are considered. Various objectives are entertained, some leading to convex formulations and some that do not. In all cases, the performance of the proposed procedure is very satisfactory and, in terms of computational cost, the scalability is manifestly 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 Work supported by the European Research Council under the H2020 Framework Programme/ERC grant 694974, by the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), and by the ICREA Academia program. Parts of this paper were presented at the 2019 IEEE Int’l Symp. Personal, Indoor & Mobile Radio Communications and at the 2020 IEEE Int’l Conf. Communications.
• dc.format.mimetype application/pdf
• dc.identifier.citation Nikbakht R, Jonsson A, Lozano A. Unsupervised learning for C-RAN power control and power allocation. IEEE Commun Lett. 2021;25(3):687-91. DOI:10.1109/LCOMM.2020.3027991
• dc.identifier.doi http://dx.doi.org/10.1109/LCOMM.2020.3027991
• dc.identifier.issn 1089-7798
• dc.identifier.uri http://hdl.handle.net/10230/47547
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Communications Letters. 2021;25(3):687-91
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/694974
• 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.2020.3027991
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Neural networks
• dc.subject.keyword Unsupervised learning
• dc.subject.keyword Cell-free networks
• dc.subject.keyword Ultradense networks
• dc.subject.keyword Power control
• dc.subject.keyword Power allocation
• dc.subject.keyword C-RAN
• dc.title Unsupervised learning for C-RAN power control and power allocation
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion