Unsupervised learning for C-RAN power control and power allocation

Abstract

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.