  Channel estimation and hybrid precoding are considered for multi-user
millimeter wave massive multi-input multi-output system. A deep learning
compressed sensing (DLCS) channel estimation scheme is proposed. The channel
estimation neural network for the DLCS scheme is trained offline using
simulated environments to predict the beamspace channel amplitude. Then the
channel is reconstructed based on the obtained indices of dominant beamspace
channel entries. A deep learning quantized phase (DLQP) hybrid precoder design
method is developed after channel estimation. The training hybrid precoding
neural network for the DLQP method is obtained offline considering the
approximate phase quantization. Then the deployment hybrid precoding neural
network (DHPNN) is obtained by replacing the approximate phase quantization
with ideal phase quantization and the output of the DHPNN is the analog
precoding vector. Finally, the analog precoding matrix is obtained by stacking
the analog precoding vectors and the digital precoding matrix is calculated by
zero-forcing. Simulation results demonstrate that the DLCS channel estimation
scheme outperforms the existing schemes in terms of the normalized mean-squared
error and the spectral efficiency, while the DLQP hybrid precoder design method
has better spectral efficiency performance than other methods with low phase
shifter resolution.
