  Efficient resource allocation with hybrid precoder design is essential for
massive MIMO systems operating in millimeter wave (mmW) domain. Owing to a
higher energy efficiency and a lower complexity of a partially connected hybrid
architecture, in this letter, we propose a joint deep convolutional neural
network (CNN) based scheme for precoder design and antenna selection of a
partially connected massive MIMO hybrid system. Precoder design and antenna
selection is formulated as a regression and classification problem,
respectively, for CNN. The channel data is fed to the first CNN network which
outputs a subset of selected antennas having the optimal spectral efficiency.
This subset is again fed to the second CNN to obtain the block diagonal
precoder for a partially connected architecture. Simulation results verifies
the superiority of CNN based approach over conventional iterative and
alternating minimization (alt-min) algorithms. Moreover, the proposed scheme is
computationally efficient and is not very sensitive to channel irregularities.
