  This paper introduces a new efficient autoprecoder (AP) based deep learning
approach for massive multiple-input multiple-output (mMIMO) downlink systems in
which the base station is equipped with a large number of antennas with
energy-efficient power amplifiers (PAs) and serves multiple user terminals. We
present AP-mMIMO, a new method that jointly eliminates the multiuser
interference and compensates the severe nonlinear (NL) PA distortions. Unlike
previous works, AP-mMIMO has a low computational complexity, making it suitable
for a global energy-efficient system. Specifically, we aim to design the
PA-aware precoder and the receive decoder by leveraging the concept of
autoprecoder, whereas the end-to-end massive multiuser (MU)-MIMO downlink is
designed using a deep neural network (NN). Most importantly, the proposed
AP-mMIMO is suited for the varying block fading channel scenario. To deal with
such scenarios, we consider a two-stage precoding scheme: 1) a NN-precoder is
used to address the PA non-linearities and 2) a linear precoder is used to
suppress the multiuser interference. The NN-precoder and the receive decoder
are trained off-line and when the channel varies, only the linear precoder
changes on-line. This latter is designed by using the widely used zero-forcing
precoding scheme or its lowcomplexity version based on matrix polynomials.
Numerical simulations show that the proposed AP-mMIMO approach achieves
competitive performance with a significantly lower complexity compared to
existing literature. Index Terms-multiuser (MU) precoding, massive
multipleinput multiple-output (MIMO), energy-efficiency, hardware impairment,
power amplifier (PA) nonlinearities, autoprecoder, deep learning, neural
network (NN)
