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In this paper, a novel end-to-end learning approach, namely JTRD-Net, isproposed for uplink multiuser single-input multiple-output (MU-SIMO) jointtransmitter and non-coherent receiver design (JTRD) in fading channels. Thebasic idea lies in the use of artificial neural networks (ANNs) to replacetraditional communication modules at both transmitter and receiver sides. Morespecifically, the transmitter side is modeled as a group of parallel linearlayers, which are responsible for multiuser waveform design; and thenon-coherent receiver is formed by a deep feed-forward neural network (DFNN) soas to provide multiuser detection (MUD) capabilities. The entire JTRD-Net canbe trained from end to end to adapt to channel statistics through deeplearning. After training, JTRD-Net can work efficiently in a non-coherentmanner without requiring any levels of channel state information (CSI). Inaddition to the network architecture, a novel weight-initialization method,namely symmetrical-interval initialization, is proposed for JTRD-Net. It isshown that the symmetrical-interval initialization outperforms the conventionalmethod (e.g. Xavier initialization) in terms of well-balanced convergence-rateamong users. Simulation results show that the proposed JTRD-Net approach takessignificant advantages in terms of reliability and scalability over baselineschemes on both i.i.d. complex Gaussian channels and spatially-correlatedchannels.

End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels