  In this paper, we propose an end-to-end deep learning-based joint transceiver
design algorithm for millimeter wave (mmWave) massive multiple-input
multiple-output (MIMO) systems, which consists of deep neural network
(DNN)-aided pilot training, channel feedback, and hybrid analog-digital (HAD)
precoding. Specifically, we develop a DNN architecture that maps the received
pilots into feedback bits at the receiver, and then further maps the feedback
bits into the hybrid precoder at the transmitter. To reduce the signaling
overhead and channel state information (CSI) mismatch caused by the
transmission delay, a two-timescale DNN composed of a long-term DNN and a
short-term DNN is developed. The analog precoders are designed by the long-term
DNN based on the CSI statistics and updated once in a frame consisting of a
number of time slots. In contrast, the digital precoders are optimized by the
short-term DNN at each time slot based on the estimated low-dimensional
equivalent CSI matrices. A two-timescale training method is also developed for
the proposed DNN with a binary layer. We then analyze the generalization
ability and signaling overhead for the proposed DNN based algorithm. Simulation
results show that our proposed technique significantly outperforms conventional
schemes in terms of bit-error rate performance with reduced signaling overhead
and shorter pilot sequences.
