Nikbakht Silab, RasoulJonsson, Anders, 1973-Lozano Solsona, Angel2020-07-302020Nikbakht R, Jonsson A, Lozano A. Unsupervised-learning power allocation for the cell-free downlink. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops); 2020 Jun 7-11; Dublin, Ireland. [New York]: IEEE; 2020. [5 p.] DOI: 10.1109/ICCWorkshops49005.2020.91451462474-9133http://hdl.handle.net/10230/45220This paper applies feedforward neural networks to the problem of centralized power allocation in the downlink of cell-free wireless systems with conjugate beamforming. The formulation relies only on large-scale channel gains. Most importantly, the learning is unsupervised, foregoing the taxing precomputation of training data that supervised learning would require. Two loss metrics are entertained, namely (i) the max-min of the user signal-to-interference ratios (SIRs), or more precisely a generalized form of maxmin that can be softened at will to regulate the tradeoff between average performance and fairness, and (ii) the maxproduct of the SIRs, which intrinsically effects such tradeoff.application/pdfeng© 2020 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/ICCWorkshops49005.2020.9145146info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ICCWorkshops49005.2020.9145146info:eu-repo/semantics/openAccess