Nikbakht Silab, RasoulJonsson, Anders, 1973-Lozano Solsona, Angel2021-05-132021-05-132021Nikbakht 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.2020.30279911089-7798http://hdl.handle.net/10230/47547This 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.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.2020.3027991info:eu-repo/semantics/articlehttp://dx.doi.org/10.1109/LCOMM.2020.3027991Neural networksUnsupervised learningCell-free networksUltradense networksPower controlPower allocationC-RANinfo:eu-repo/semantics/openAccess