  This paper shows that deep neural network (DNN) can be used for efficient and
distributed channel estimation, quantization, feedback, and downlink multiuser
precoding for a frequency-division duplex massive multiple-input
multiple-output system in which a base station (BS) serves multiple mobile
users, but with rate-limited feedback from the users to the BS. A key
observation is that the multiuser channel estimation and feedback problem can
be thought of as a distributed source coding problem. In contrast to the
traditional approach where the channel state information (CSI) is estimated and
quantized at each user independently, this paper shows that a joint design of
pilots and a new DNN architecture, which maps the received pilots directly into
feedback bits at the user side then maps the feedback bits from all the users
directly into the precoding matrix at the BS, can significantly improve the
overall performance. This paper further proposes robust design strategies with
respect to channel parameters and also a generalizable DNN architecture for
varying number of users and number of feedback bits. Numerical results show
that the DNN-based approach with short pilot sequences and very limited
feedback overhead can already approach the performance of conventional linear
precoding schemes with full CSI.
