  Hybrid beamformer design plays very crucial role in the next generation
millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output)
systems. Previous works assume the perfect channel state information (CSI)
which results heavy feedback overhead. To lower complexity, channel statistics
can be utilized such that only infrequent update of the channel information is
needed. To reduce the complexity and provide robustness, in this work, we
propose a deep learning (DL) framework to deal with both hybrid beamforming and
channel estimation. For this purpose, we introduce three deep convolutional
neural network (CNN) architectures. We assume that the base station (BS) has
the channel statistics only and feeds the channel covariance matrix into a CNN
to obtain the hybrid precoders. At the receiver, two CNNs are employed. The
first one is used for channel estimation purposes and the another is employed
to design the hybrid combiners. The proposed DL framework does not require the
instantaneous feedback of the CSI at the BS. We have shown that the proposed
approach has higher spectral efficiency with comparison to the conventional
techniques. The trained CNN structures do not need to be re-trained due to the
changes in the propagation environment such as the deviations in the number of
received paths and the fluctuations in the received path angles up to 4
degrees. Also, the proposed DL framework exhibits at least 10 times lower
computational complexity as compared to the conventional optimization-based
approaches.
