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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Vydané v:	IEEE transactions on wireless communications Ročník 20; číslo 7; s. 4044 - 4057
Hlavní autori:	Sohrabi, Foad, Attiah, Kareem M., Yu, Wei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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