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

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 t...

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Veröffentlicht in:	IEEE transactions on wireless communications 2021-09, Vol.20 (9), p.5531-5542
Hauptverfasser:	Xue, Songyan, Ma, Yi, Yi, Na
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Channels Coherence Computer architecture Deep learning End-to-end learning Fading joint transmitter and receiver design MIMO communication Multiuser detection multiuser detection (MUD) multiuser single-input multiple-output (MU-SIMO) Neural networks Receivers Training Transmitters Uplink Waveforms weight initialization Wireless communication
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