  In conventional multi-user multiple-input multiple-output (MU-MIMO) systems
with frequency division duplexing (FDD), channel acquisition and precoder
optimization processes have been designed separately although they are highly
coupled. This paper studies an end-to-end design of downlink MU-MIMO systems
which include pilot sequences, limited feedback, and precoding. To address this
problem, we propose a novel deep learning (DL) framework which jointly
optimizes the feedback information generation at users and the precoder design
at a base station (BS). Each procedure in the MU-MIMO systems is replaced by
intelligently designed multiple deep neural networks (DNN) units. At the BS, a
neural network generates pilot sequences and helps the users obtain accurate
channel state information. At each user, the channel feedback operation is
carried out in a distributed manner by an individual user DNN. Then, another BS
DNN collects feedback information from the users and determines the MIMO
precoding matrices. A joint training algorithm is proposed to optimize all DNN
units in an end-to-end manner. In addition, a training strategy which can avoid
retraining for different network sizes for a scalable design is proposed.
Numerical results demonstrate the effectiveness of the proposed DL framework
compared to classical optimization techniques and other conventional DNN
schemes.
