  In this paper, a novel end-to-end learning approach, namely JTRD-Net, is
proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint
transmitter and non-coherent receiver design (JTRD) in fading channels. The
basic idea lies in the use of artificial neural networks (ANNs) to replace
traditional communication modules at both transmitter and receiver sides. More
specifically, the transmitter side is modeled as a group of parallel linear
layers, which are responsible for multiuser waveform design; and the
non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so
as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can
be trained from end to end to adapt to channel statistics through deep
learning. After training, JTRD-Net can work efficiently in a non-coherent
manner without requiring any levels of channel state information (CSI). In
addition to the network architecture, a novel weight-initialization method,
namely symmetrical-interval initialization, is proposed for JTRD-Net. It is
shown that the symmetrical-interval initialization outperforms the conventional
method (e.g. Xavier initialization) in terms of well-balanced convergence-rate
among users. Simulation results show that the proposed JTRD-Net approach takes
significant advantages in terms of reliability and scalability over baseline
schemes on both i.i.d. complex Gaussian channels and spatially-correlated
channels.
