  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.
