  In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO)
systems, hybrid precoding is a crucial task to lower the complexity and cost
while achieving a sufficient sum-rate. Previous works on hybrid precoding were
usually based on optimization or greedy approaches. These methods either
provide higher complexity or have sub-optimum performance. Moreover, the
performance of these methods mostly relies on the quality of the channel data.
In this work, we propose a deep learning (DL) framework to improve the
performance and provide less computation time as compared to conventional
techniques. In fact, we design a convolutional neural network for MIMO
(CNN-MIMO) that accepts as input an imperfect channel matrix and gives the
analog precoder and combiners at the output. The procedure includes two main
stages. First, we develop an exhaustive search algorithm to select the analog
precoder and combiners from a predefined codebook maximizing the achievable
sum-rate. Then, the selected precoder and combiners are used as output labels
in the training stage of CNN-MIMO where the input-output pairs are obtained. We
evaluate the performance of the proposed method through numerous and extensive
simulations and show that the proposed DL framework outperforms conventional
techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the
presence of imperfections regarding the channel matrix. On top of this, the
proposed approach exhibits less computation time with comparison to the
optimization and codebook based approaches.
