# Deep Learning Power Allocation in Massive MIMO

## TL;DR

This paper proposes a deep learning approach to efficiently perform power allocation in Massive MIMO networks, achieving near-optimal performance with reduced computational complexity by mapping user positions to power policies.

## Contribution

It introduces a neural network model that predicts power allocation in Massive MIMO, eliminating the need for statistical averaging and traditional optimization methods.

## Key findings

- Achieves near-optimal power allocation performance.
- Reduces computational complexity compared to traditional methods.
- Does not require statistical averaging in the optimization process.

## Abstract

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.