  In millimeter-wave communications, multiple-input-multiple-output (MIMO)
systems use large antenna arrays to achieve high gain and spectral efficiency.
These massive MIMO systems employ hybrid beamformers to reduce power
consumption associated with fully digital beamforming in large arrays. Further
savings in cost and power are possible through the use of subarrays. Unlike
prior works that resort to large latency methods such as optimization and
greedy search for subarray selection, we propose a deep-learning-based approach
in order to overcome the complexity issue without causing significant
performance loss. We formulate antenna selection and hybrid beamformer design
as a classification/prediction problem for convolutional neural networks
(CNNs). For antenna selection, the CNN accepts the channel matrix as input and
outputs a subarray with optimal spectral efficiency. The resultant subarray
channel matrix is then again fed to a CNN to obtain analog and baseband
beamformers. We train the CNNs with several noisy channel matrices that have
different channel statistics in order to achieve a robust performance at the
network output. Numerical experiments show that our CNN framework provides an
order better spectral efficiency and is 10 times faster than the conventional
techniques. Further investigations with quantized-CNNs show that the proposed
network, saved in no more than 5 bits, is also suited for digital mobile
devices.
