  In this paper, a general framework for deep learning-based power control
methods for max-min, max-product and max-sum-rate optimization in uplink
cell-free massive multiple-input multiple-output (CF mMIMO) systems is
proposed. Instead of using supervised learning, the proposed method relies on
unsupervised learning, in which optimal power allocations are not required to
be known, and thus has low training complexity. More specifically, a deep
neural network (DNN) is trained to learn the map between fading coefficients
and power coefficients within short time and with low computational complexity.
It is interesting to note that the spectral efficiency of CF mMIMO systems with
the proposed method outperforms previous optimization methods for max-min
optimization and fits well for both max-sum-rate and max-product optimizations.
