  Hybrid beamforming (HBF) and antenna selection are promising techniques for
improving the energy efficiency~(EE) of massive multiple-input
multiple-output~(mMIMO) systems. However, the transmitter architecture may
contain several parameters that need to be optimized, such as the power
allocated to the antennas and the connections between the antennas and the
radio frequency chains. Therefore, finding the optimal transmitter architecture
requires solving a non-convex mixed integer problem in a large search space. In
this paper, we consider the problem of maximizing the EE of fully digital
precoder~(FDP) and hybrid beamforming~(HBF) transmitters. First, we propose an
energy model for different beamforming structures. Then, based on the proposed
energy model, we develop an unsupervised deep learning method to maximize the
EE by designing the transmitter configuration for FDP and HBF. The proposed
deep neural networks can provide different trade-offs between spectral
efficiency and energy consumption while adapting to different numbers of active
users. Finally, to ensure that the proposed method can be implemented in
practice, we investigate the ability of the model to be trained exclusively
using imperfect channel state information~(CSI), both for the input to the deep
learning model and for the calculation of the loss function. Simulation results
show that the proposed solutions can outperform conventional methods in terms
of EE while being trained with imperfect CSI. Furthermore, we show that the
proposed solutions are less complex and more robust to noise than conventional
methods.
