An overview of the CSI feedback based on deep learning for massive MIMO systems

The massive multiple-input multiple-output (MIMO) technology is considered to be one of the core technologies of the next generation communication system.To fully utilize the potential gains of MIMO systems,the base station should accurately acquire the channel state information (CSI).Due to the sig...

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Main Authors: Muhan CHEN, Jiajia GUO, Xiao LI, Shi JIN
Format: Article
Language:zho
Published: China InfoCom Media Group 2020-03-01
Series:物联网学报
Subjects:
Online Access:http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00157/
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author Muhan CHEN
Jiajia GUO
Xiao LI
Shi JIN
author_facet Muhan CHEN
Jiajia GUO
Xiao LI
Shi JIN
author_sort Muhan CHEN
collection DOAJ
description The massive multiple-input multiple-output (MIMO) technology is considered to be one of the core technologies of the next generation communication system.To fully utilize the potential gains of MIMO systems,the base station should accurately acquire the channel state information (CSI).Due to the significant increase in the number of antennas,the traditional feedback schemes based on the codebook or vector quantization are faced with great technical challenges.Recently,deep learning (DL) has provided a new idea for solving CSI feedback problems in massive MIMO systems.It was focused on the key technologies of the CSI feedback for massive MIMO systems.Firstly,the background and significance of the CSI feedback were expounded.Then,a model for the massive MIMO system was established and the sparse nature of CSI was analyzed.Several schemes of introducing DL into the CSI feedback mechanism were introduced and compared in detail.Finally,a further prospect on the development trend of the CSI feedback based on DL was made.
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institution Kabale University
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series 物联网学报
spelling doaj-art-af7d45d01d2342e4ae94b42daf8a69e62025-01-15T02:52:48ZzhoChina InfoCom Media Group物联网学报2096-37502020-03-014334459645534An overview of the CSI feedback based on deep learning for massive MIMO systemsMuhan CHENJiajia GUOXiao LIShi JINThe massive multiple-input multiple-output (MIMO) technology is considered to be one of the core technologies of the next generation communication system.To fully utilize the potential gains of MIMO systems,the base station should accurately acquire the channel state information (CSI).Due to the significant increase in the number of antennas,the traditional feedback schemes based on the codebook or vector quantization are faced with great technical challenges.Recently,deep learning (DL) has provided a new idea for solving CSI feedback problems in massive MIMO systems.It was focused on the key technologies of the CSI feedback for massive MIMO systems.Firstly,the background and significance of the CSI feedback were expounded.Then,a model for the massive MIMO system was established and the sparse nature of CSI was analyzed.Several schemes of introducing DL into the CSI feedback mechanism were introduced and compared in detail.Finally,a further prospect on the development trend of the CSI feedback based on DL was made.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00157/massive MIMOdeep learningCSI feedback
spellingShingle Muhan CHEN
Jiajia GUO
Xiao LI
Shi JIN
An overview of the CSI feedback based on deep learning for massive MIMO systems
物联网学报
massive MIMO
deep learning
CSI feedback
title An overview of the CSI feedback based on deep learning for massive MIMO systems
title_full An overview of the CSI feedback based on deep learning for massive MIMO systems
title_fullStr An overview of the CSI feedback based on deep learning for massive MIMO systems
title_full_unstemmed An overview of the CSI feedback based on deep learning for massive MIMO systems
title_short An overview of the CSI feedback based on deep learning for massive MIMO systems
title_sort overview of the csi feedback based on deep learning for massive mimo systems
topic massive MIMO
deep learning
CSI feedback
url http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00157/
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