  We propose a method for channel training and precoding in FDD massive MIMO
based on deep neural networks (DNNs), exploiting Downlink (DL) channel
covariance knowledge. The DNN is optimized to maximize the DL multi-user
sum-rate, by producing a pre-beamforming matrix based on user channel
covariances that maps the original channel vectors to effective channels.
Measurements of these effective channels are received at the users via common
pilot transmission and sent back to the base station (BS) through analog
feedback without further processing. The BS estimates the effective channels
from received feedback and constructs a linear precoder by concatenating the
optimized pre-beamforming matrix with a zero-forcing precoder over the
effective channels. We show that the proposed method yields significantly
higher sum-rates than the state-of-the-art DNN-based channel training and
precoding scheme, especially in scenarios with small pilot and feedback size
relative to the channel coherence block length. Unlike many works in the
literature, our proposition does not involve deployment of a DNN at the user
side, which typically comes at a high computational cost and
parameter-transmission overhead on the system, and is therefore considerably
more practical.
