Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO

This paper shows that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequency-division duplex massive multiple-input multiple-output system in which a base station (BS) serves multiple mobile use...

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Veröffentlicht in:	IEEE transactions on wireless communications Jg. 20; H. 7; S. 4044 - 4057
Hauptverfasser:	Sohrabi, Foad, Attiah, Kareem M., Yu, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Channel estimation Deep learning deep neural network (DNN) distributed source coding (DSC) Downlink downlink precoding Estimation Feedback feedback frequency-division duplex (FDD) Frequency division duplexing Machine learning massive multiple-input multiple-output (MIMO) Precoding quantization Quantization (signal) Radio equipment Robust design Training
ISSN:	1536-1276, 1558-2248
Online-Zugang:	Volltext
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