Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities

This paper introduces a new efficient autopre-coder (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 termin...

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Veröffentlicht in:	IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC S. 1039 - 1044
Hauptverfasser:	Cheng, Xinying, Zayani, Rafik, Ferecatu, Marin, Audebert, Nicolas
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 10.04.2022
Schlagworte:	Artificial neural networks autoprecoder Channel estimation Deep learning Downlink energy-efficiency Fading channels hardware impairment Interference massive multipleinput multiple-output (MIMO) multi-user (MU) precoding neural network (NN) power amplifier (PA) nonlinearities Precoding
ISSN:	1558-2612
Online-Zugang:	Volltext
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