  This paper aims to handle the joint transmitter and noncoherent receiver
design for multiuser multiple-input multiple-output (MU-MIMO) systems through
deep learning. Given the deep neural network (DNN) based noncoherent receiver,
the novelty of this work mainly lies in the multiuser waveform design at the
transmitter side. According to the signal format, the proposed deep learning
solutions can be divided into two groups. One group is called pilot-aided
waveform, where the information-bearing symbols are time-multiplexed with the
pilot symbols. The other is called learning-based waveform, where the multiuser
waveform is partially or even completely designed by deep learning algorithms.
Specifically, if the information-bearing symbols are directly embedded in the
waveform, it is called systematic waveform. Otherwise, it is called
non-systematic waveform, where no artificial design is involved. Simulation
results show that the pilot-aided waveform design outperforms the conventional
zero forcing receiver with least squares (LS) channel estimation on small-size
MU-MIMO systems. By exploiting the time-domain degrees of freedom (DoF), the
learning-based waveform design further improves the detection performance by at
least 5 dB at high signal-to-noise ratio (SNR) range. Moreover, it is found
that the traditional weight initialization method might cause a training
imbalance among different users in the learning-based waveform design. To
tackle this issue, a novel weight initialization method is proposed which
provides a balanced convergence performance with no complexity penalty.
