Nikbakht Silab, RasoulJonsson, Anders, 1973-Lozano Solsona, Angel2019-11-262019Nikbakht 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.2019.8904394978-1-5386-8110-72166-9589http://hdl.handle.net/10230/42994Comunicació 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.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.application/pdfeng© 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.2019.8904394info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PIMRC.2019.8904394info:eu-repo/semantics/openAccess