  In broadband millimeter-wave (mm-Wave) systems, it is desirable to design
hybrid beamformers with common analog beamformer for the entire band while
employing different baseband beamformers in different frequency sub-bands.
Furthermore, the performance mostly relies on the perfectness of the channel
information. In this paper, we propose a deep learning (DL) framework for
hybrid beamformer design in broadband mm-Wave massive MIMO systems. We design a
convolutional neural network (CNN) that accepts the channel matrix of all
subcarriers as input and the output of CNN is the hybrid beamformers. The
proposed CNN architecture is trained with imperfect channel matrices in order
to provide robust performance against the deviations in the channel data.
Hence, the proposed precoding scheme can handle the imperfect or limited
feedback scenario where the full and exact knowledge of the channel is not
available. We show that the proposed DL framework is more robust and
computationally less complex than the conventional optimization and
phase-extraction-based approaches.
