Unsupervised-learning power control for cell-free wireless systems

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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 2019-11-26T13:50:09Z
• dc.date.issued 2019
• dc.description Comunicació presentada a: 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) celebrat del 8 a l'11 de novembre de 2019 a Istambul, Turquia.
• dc.description.abstract This paper studies the viability of feedforward neural networks (NNs) for centralized power control in the uplink of cell-free wireless systems with matched-filter reception. The formulation relies only on large-scale channel behaviors as inputs, without the need for user location information, and on unsupervised learning, to avoid the onerous precomputation of training data that supervised learning would necessitate for every system or environment modification. Two different power control objectives are entertained, and for both of them the NN closely approximates the optimum solutions produced by convex solvers while vastly reducing the complexity, thereby opening the door to power control implementations for very large systems.
• dc.description.sponsorship This work was supported by the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the European Research Council under the H2020 Framework Programme/ERC grant agreement 694974.
• dc.format.mimetype application/pdf
• dc.identifier.citation Nikbakht R, Jonsson A, Lozano A. Unsupervised-learning power control for cell-free wireless systems. In: 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): Track 1: Fundamentals and PHY; 2019 Sep 8-11; Istanbul, Turkey. New Jersey: Institute of Electrical and Electronics Engineers; 2019. DOI: 10.1109/PIMRC.2019.8904394
• dc.identifier.doi http://dx.doi.org/10.1109/PIMRC.2019.8904394
• dc.identifier.isbn 978-1-5386-8110-7
• dc.identifier.issn 2166-9589
• dc.identifier.uri http://hdl.handle.net/10230/42994
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers
• dc.relation.ispartof 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): Track 1: Fundamentals and PHY; 2019 Sep 8-11; Istanbul, Turkey. New Jersey: Institute of Electrical and Electronics Engineers; 2019.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/694974
• dc.rights © 2019 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/PIMRC.2019.8904394
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.title Unsupervised-learning power control for cell-free wireless systems
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion