This 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.

Work supported by the European Research Council under the H2020 Framework Programme/ERC grant 694974, by the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), and by the ICREA Academia program. Parts of this paper were presented at the 2019 IEEE Int’l Symp. Personal, Indoor & Mobile Radio Communications and at the 2020 IEEE Int’l Conf. Communications.