# Deep Learning Power Allocation in Massive MIMO

arXiv: https://arxiv.org/abs/1812.03640 · HTML: https://mlsanity.com/paper/1812.03640/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

## Built on by (most cited first, 13 of 13)

- [Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network](https://mlsanity.com/paper/2101.12090/) (2021)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Deep Learning-based Power Control for Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2102.10366/) (2021)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System](https://mlsanity.com/paper/2110.04731/) (2021)
- [Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training](https://mlsanity.com/paper/2206.06592/) (2022)
- [Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework](https://mlsanity.com/paper/2305.09739/) (2023)
- [To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks](https://mlsanity.com/paper/2507.17494/) (2025)
- [Learning to Perform Downlink Channel Estimation in Massive MIMO Systems](https://mlsanity.com/paper/2109.02463/) (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)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers](https://mlsanity.com/paper/2404.15344/) (2024)

## Abstract (arXiv)

This work advocates the use of deep learning to perform max-min and max-prod power allocation in the downlink of Massive MIMO networks. More precisely, a deep neural network is trained to learn the map between the positions of user equipments (UEs) and the optimal power allocation policies, and then used to predict the power allocation profiles for a new set of UEs' positions. The use of deep learning significantly improves the complexity-performance trade-off of power allocation, compared to traditional optimization-oriented methods. Particularly, the proposed approach does not require the computation of any statistical average, which would be instead necessary by using standard methods, and is able to guarantee near-optimal performance.