Nikbakht Silab, RasoulJonsson, Anders, 1973-Lozano Solsona, Angel2021-05-132021-05-132021Nikbakht R, Jonsson A, Lozano A. Unsupervised learning for cellular power control. IEEE Commun Lett. 2021;25(3):682-6. DOI: 10.1109/LCOMM.2020.30279941089-7798http://hdl.handle.net/10230/47548This 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.application/pdfeng© 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/articlehttp://dx.doi.org/10.1109/LCOMM.2020.3027994Machine learningNeural networksUnsupervised learningPower controlCellular systemsinfo:eu-repo/semantics/openAccess