Unsupervised-learning power allocation for the cell-free downlink

Abstract

This 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.