  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.
