Deep Learning-based Power Control for Cell-Free Massive MIMO Networks

TL;DR

This paper introduces an unsupervised deep learning approach for power control in cell-free massive MIMO networks, achieving near-optimal user fairness with significantly reduced computational complexity.

Contribution

It presents a novel unsupervised deep neural network method for max-min power control, eliminating the need for labeled training data and enabling faster, scalable solutions.

Findings

Achieves 400x faster implementation than traditional algorithms.

Provides near-optimal performance with 4-6x faster online learning.

Demonstrates effective max-min fairness in cell-free MIMO systems.

Abstract

A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Max-min rate optimization problem in a cell-free massive MIMO uplink setup is formulated, where user power allocations are optimized in order to maximize the minimum user rate. Instead of modeling the problem using mathematical optimization theory, and solving it with iterative algorithms, our proposed solution approach is using DL. Specifically, we model a deep neural network (DNN) and train it in an unsupervised manner to learn the optimum user power allocations which maximize the minimum user rate. This novel unsupervised learning-based approach does not require optimal power allocations to be known during model training as in previously used supervised learning techniques, hence it has a simpler and flexible model training stage. Numerical results show that the proposed DNN achieves a performance-complexity trade-off with around 400 times faster implementation and comparable performance to the optimization-based algorithm. An online learning stage is also introduced, which results in near-optimal performance with 4-6 times faster processing.