# Uplink power control in cell-free massive MIMO via deep learning

arXiv: https://arxiv.org/abs/1908.11121 · HTML: https://mlsanity.com/paper/1908.11121/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Deep Learning-based Power Control for Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2102.10366/) (2021)
- [Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2110.09001/) (2021)
- [Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems](https://mlsanity.com/paper/2112.14423/) (2021)

## Abstract (arXiv)

This paper focuses on the use of a deep learning approach to perform sum-rate-max and max-min power allocation in the uplink of a cell-free massive MIMO network. In particular, we train a deep neural network in order to learn the mapping between a set of input data and the optimal solution of the power allocation strategy. Numerical results show that the presence of the pilot contamination in the cell-free massive MIMO system does not significantly affect the learning capabilities of the neural network, that gives near-optimal performance. Conversely, with the introduction of the shadowing effect in the system the performance obtained with the deep learning approach gets significantly degraded with respect to the optimal one.