Unsupervised-learning power control for cell-free wireless systems

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To access the full text documents, please follow this link: http://hdl.handle.net/10230/42994

dc.contributor.author	Nikbakht, Rasoul
dc.contributor.author	Jonsson, Anders, 1973-
dc.contributor.author	Lozano Solsona, Angel
dc.date	2019
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.citation	978-1-5386-8110-7
dc.identifier.citation	2166-9589
dc.identifier.citation	2166-9589
dc.identifier.citation	http://dx.doi.org/10.1109/PIMRC.2019.8904394
dc.identifier.uri	http://hdl.handle.net/10230/42994
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	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	info:eu-repo/grantAgreement/EC/H2020/694974
dc.rights	info:eu-repo/semantics/embargoedAccess
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.title	Unsupervised-learning power control for cell-free wireless systems
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/acceptedVersion
dc.description.abstract	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.abstract	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.

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