A Deep Learning-Based Framework for Low Complexity Multiuser MIMO Precoding Design

Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional al...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on wireless communications Ročník 21; číslo 12; s. 11193 - 11206
Hlavní autoři:	Zhang, Maojun, Gao, Jiabao, Zhong, Caijun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Artificial neural networks Complexity Computational complexity Deep learning Downlink Machine learning MIMO communication MIMO-OFDM MISO communication model compression Multi-user MIMO Orthogonal Frequency Division Multiplexing precoder design Precoding Resource management Wireless communication
ISSN:	1536-1276, 1558-2248
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity.
AbstractList	Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity.
Author	Zhang, Maojun Zhong, Caijun Gao, Jiabao
Author_xml	– sequence: 1 givenname: Maojun surname: Zhang fullname: Zhang, Maojun email: zhmj@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Jiabao surname: Gao fullname: Gao, Jiabao email: gao_jiabao@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China – sequence: 3 givenname: Caijun orcidid: 0000-0002-9960-7800 surname: Zhong fullname: Zhong, Caijun email: caijunzhong@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
BookMark	eNp9kEtPAjEURhujiYDuTdw0cT3Yx7Qzs0QUJYFgDMblpFNuSRGmYzsE-feWQFy4cHXv4jv3cbrovHY1IHRDSZ9SUtzPP4Z9Rhjrc1qQlIsz1KFC5AljaX5-6LlMKMvkJeqGsCKEZlKIDnob4EeABk9A-drWy-RBBVjgkVcb2Dn_iY3zeOJ2eOg2zRq-bbvH0-26tdsAHk_H0xl-9aDdIrJxUrDL-gpdGLUOcH2qPfQ-epoPX5LJ7Hk8HEwSzQraJloYIVUuuMmZzKSU2miqDcmYyZUwXBU5zwmwKq3i2RWtSLrgCwaQscpkOuU9dHec23j3tYXQliu39XVcWbIszYXkBcliSh5T2rsQPJhS21a11tWtV3ZdUlIe_JXRX3nwV578RZD8ARtvN8rv_0Nuj4gFgN94_CON-vkP00t78Q
CODEN	ITWCAX
CitedBy_id	crossref_primary_10_1109_ACCESS_2023_3297979 crossref_primary_10_1109_TCCN_2023_3235763 crossref_primary_10_1109_JSAC_2024_3431583 crossref_primary_10_1109_TWC_2024_3435023 crossref_primary_10_3390_app131910855 crossref_primary_10_1109_TVT_2024_3452409 crossref_primary_10_3390_app142411994 crossref_primary_10_1109_TWC_2023_3305124 crossref_primary_10_3390_electronics14112277 crossref_primary_10_1109_TWC_2025_3527634 crossref_primary_10_1109_JSAC_2025_3531537 crossref_primary_10_1109_TWC_2025_3562818 crossref_primary_10_1109_TCCN_2024_3488815 crossref_primary_10_1109_TWC_2024_3509713 crossref_primary_10_1109_TCCN_2024_3382973 crossref_primary_10_1109_TNSE_2025_3563370 crossref_primary_10_1109_TVT_2023_3293848 crossref_primary_10_1109_TWC_2025_3548682 crossref_primary_10_1007_s11235_024_01135_4
Cites_doi	10.1109/TWC.2020.3033334 10.1109/LWC.2020.3007198 10.1109/LCOMM.2020.3041510 10.1109/TVT.2020.2980905 10.1109/TIT.2003.813523 10.1109/LCOMM.2020.2965532 10.1109/JPROC.2019.2957798 10.1109/JSAC.2020.3041383 10.1109/MSP.2014.2312183 10.1109/TSP.2020.2976585 10.1109/TSP.2006.890905 10.1109/COMST.2019.2926625 10.1109/ISCC50000.2020.9219669 10.1109/JSAC.2017.2699338 10.1109/MWC.2019.1800447 10.1109/JSTSP.2019.2925975 10.24963/ijcai.2018/309 10.1109/TSP.2019.2905833 10.1109/TSP.2018.2866382 10.1109/TWC.2021.3107452 10.1109/TCOMM.2019.2924010 10.1109/TSP.2019.2908906 10.1109/TCOMM.2019.2960361 10.1109/LWC.2018.2832128 10.1109/JSAC.2019.2933962 10.1109/GLOBECOM42002.2020.9322310 10.1109/LWC.2017.2757490 10.1109/JSAC.2005.862421 10.1109/TVT.2019.2897134 10.1109/TCOMM.2018.2823715 10.1109/T-WC.2008.070851 10.1109/TSP.2010.2053709 10.1109/TSP.2019.2899805 10.1109/TCOMM.2004.840638 10.1109/TSP.2011.2147784 10.1109/TSP.2003.821107 10.1109/ACCESS.2018.2887308
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
DOI	10.1109/TWC.2022.3190435
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-2248
EndPage	11206
ExternalDocumentID	10_1109_TWC_2022_3190435 9834153
Genre	orig-research
GrantInformation_xml	– fundername: National Key Research and Development Program of China grantid: 2018YFB1801104 funderid: 10.13039/501100012166 – fundername: National Natural Science Foundation of China grantid: 61922071 funderid: 10.13039/501100001809
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AIBXA AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 IES IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c291t-c5f56a853f8267666cfc1cf072f8a5f3a98380e2b4b276b1b04d3d2ee72bf7c43
IEDL.DBID	RIE
ISICitedReferencesCount	27
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000913795700075&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1536-1276
IngestDate	Fri Jul 25 12:33:55 EDT 2025 Sat Nov 29 06:23:55 EST 2025 Tue Nov 18 22:11:35 EST 2025 Wed Aug 27 02:14:29 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	12
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c291t-c5f56a853f8267666cfc1cf072f8a5f3a98380e2b4b276b1b04d3d2ee72bf7c43
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-9960-7800
PQID	2748563907
PQPubID	105736
PageCount	14
ParticipantIDs	ieee_primary_9834153 proquest_journals_2748563907 crossref_primary_10_1109_TWC_2022_3190435 crossref_citationtrail_10_1109_TWC_2022_3190435
PublicationCentury	2000
PublicationDate	2022-Dec. 2022-12-00 20221201
PublicationDateYYYYMMDD	2022-12-01
PublicationDate_xml	– month: 12 year: 2022 text: 2022-Dec.
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on wireless communications
PublicationTitleAbbrev	TWC
PublicationYear	2022
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref35 ref13 ref34 ref12 ref15 ref36 ref14 ref31 ref30 ref33 ref11 ref32 ref10 ref2 ref1 ref17 ref38 ref16 ref19 ref18 pellaco (ref27) 2020 ref24 ref23 (ref39) 2021 ref26 ref25 ref20 ref22 ref21 li (ref37) 2016 ref28 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40
References_xml	– ident: ref3 doi: 10.1109/TWC.2020.3033334 – ident: ref30 doi: 10.1109/LWC.2020.3007198 – ident: ref26 doi: 10.1109/LCOMM.2020.3041510 – ident: ref22 doi: 10.1109/TVT.2020.2980905 – ident: ref5 doi: 10.1109/TIT.2003.813523 – ident: ref25 doi: 10.1109/LCOMM.2020.2965532 – ident: ref15 doi: 10.1109/JPROC.2019.2957798 – ident: ref23 doi: 10.1109/JSAC.2020.3041383 – ident: ref34 doi: 10.1109/MSP.2014.2312183 – ident: ref19 doi: 10.1109/TSP.2020.2976585 – ident: ref33 doi: 10.1109/TSP.2006.890905 – ident: ref35 doi: 10.1109/COMST.2019.2926625 – ident: ref10 doi: 10.1109/ISCC50000.2020.9219669 – ident: ref40 doi: 10.1109/JSAC.2017.2699338 – ident: ref31 doi: 10.1109/MWC.2019.1800447 – ident: ref21 doi: 10.1109/JSTSP.2019.2925975 – ident: ref38 doi: 10.24963/ijcai.2018/309 – ident: ref36 doi: 10.1109/TSP.2019.2905833 – ident: ref12 doi: 10.1109/TSP.2018.2866382 – ident: ref24 doi: 10.1109/TWC.2021.3107452 – ident: ref32 doi: 10.1109/TCOMM.2019.2924010 – ident: ref16 doi: 10.1109/TSP.2019.2908906 – ident: ref29 doi: 10.1109/TCOMM.2019.2960361 – ident: ref20 doi: 10.1109/LWC.2018.2832128 – year: 2021 ident: ref39 publication-title: NR and NG-RAN Overall Description – ident: ref14 doi: 10.1109/JSAC.2019.2933962 – ident: ref4 doi: 10.1109/GLOBECOM42002.2020.9322310 – ident: ref17 doi: 10.1109/LWC.2017.2757490 – ident: ref7 doi: 10.1109/JSAC.2005.862421 – ident: ref13 doi: 10.1109/TVT.2019.2897134 – ident: ref11 doi: 10.1109/TCOMM.2018.2823715 – ident: ref1 doi: 10.1109/T-WC.2008.070851 – ident: ref8 doi: 10.1109/TSP.2010.2053709 – ident: ref18 doi: 10.1109/TSP.2019.2899805 – ident: ref9 doi: 10.1109/TCOMM.2004.840638 – ident: ref2 doi: 10.1109/TSP.2011.2147784 – year: 2020 ident: ref27 article-title: Deep unfolding of the weighted MMSE beamforming algorithm publication-title: arXiv 2006 08448 – ident: ref6 doi: 10.1109/TSP.2003.821107 – ident: ref28 doi: 10.1109/ACCESS.2018.2887308 – year: 2016 ident: ref37 article-title: Pruning filters for efficient ConvNets publication-title: arXiv 1608 08710
SSID	ssj0017655
Score	2.5784314
Snippet	Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	11193
SubjectTerms	Algorithms Artificial neural networks Complexity Computational complexity Deep learning Downlink Machine learning MIMO communication MIMO-OFDM MISO communication model compression Multi-user MIMO Orthogonal Frequency Division Multiplexing precoder design Precoding Resource management Wireless communication
Title	A Deep Learning-Based Framework for Low Complexity Multiuser MIMO Precoding Design
URI	https://ieeexplore.ieee.org/document/9834153 https://www.proquest.com/docview/2748563907
Volume	21
WOSCitedRecordID	wos000913795700075&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE/IET Electronic Library customDbUrl: eissn: 1558-2248 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017655 issn: 1536-1276 databaseCode: RIE dateStart: 20020101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5W8aAH3-L6IgcvgnWzSbppj74WBVdFFL2VJp2IILvLPhT_vZNstwqK4K3QJISZdma-zGQ-gH2RK5mTJ444Shup1PLIJEJFwhhH0YXDOC4C2YS-vk6entLbGhxWd2EQMRSf4ZF_DLn8omfH_qiskSZkc2M5AzNa68ldrSpjoFuB4ZRee14ZXaUkedq4fzwlICgE4dOUq0Ds9uWCAqfKD0McvEt76X_7WobFMopkxxO1r0ANu6uw8K234BrcHbMzxD4rG6g-RyfkrwrWnhZjMYpW2VXvnXmL4Ltijj5YuI3rjy1Y57Jzw249Wva-jVbydR7r8NA-vz-9iEoChciKtDmKbOziVk4O2RGI0ARUrLNN67gWLsljJ3PaeMJRGGVIYqZpuCpkIRC1ME5bJTdgttvr4iYw7XzgKAsu0CgKktKmkbww0nK0iVNYh8ZUppktu4t7kovXLKAMnmakhcxrISu1UIeDakZ_0lnjj7FrXurVuFLgddiZqi0rf71hRjA7iSnu4nrr91nbMO_XntSk7MDsaDDGXZizb6OX4WAvfFWfK9vInQ
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fT9swED5BhwQ8MH6Kbmz4gRekZXVsp04ega0CrS0IdYK3KHbOCAm1VVtA_Pec3TSbBJq0tzycE-vOubvPPt8HcCQKJQuKxBFHaSOVWR6ZVKhIGOMou3CYJGUgm9D9fnp7m10twbf6LgwihuIz_O4fw1l-ObKPfquslaXkcxO5DB8SpUQ8v61VnxnoduA4JQHPLKPrQ0metQY3ZwQFhSCEmnEVqN3-BKHAqvLGFYf40vn4fzPbhI0qj2Qnc8NvwRIOt2H9r-6CO3B9wn4gjlnVQvUuOqWIVbLOohyLUb7KuqNn5n2C74s5e2HhPq7fuGC9i94lu_J42Uc3epOv9NiF352fg7PzqKJQiKzI4llkE5e0CwrJjmCEJqhinY2t41q4tEicLGjiKUdhlCGNmdhwVcpSIGphnLZK7kFjOBriPjDtfOooSy7QKEqTsthIXhppOdrUKWxCa6HT3Fb9xT3NxUMecAbPcrJC7q2QV1ZownE9YjzvrfEP2R2v9VquUngTDhZmy6ufb5oT0E4Tyry4_vT-qENYPR_0unn3ov_rM6z578wrVA6gMZs84hdYsU-z--nka1hhr8dRy-Q
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Deep+Learning-Based+Framework+for+Low+Complexity+Multiuser+MIMO+Precoding+Design&rft.jtitle=IEEE+transactions+on+wireless+communications&rft.au=Zhang%2C+Maojun&rft.au=Gao%2C+Jiabao&rft.au=Zhong%2C+Caijun&rft.date=2022-12-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1536-1276&rft.eissn=1558-2248&rft.volume=21&rft.issue=12&rft.spage=11193&rft_id=info:doi/10.1109%2FTWC.2022.3190435&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1536-1276&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1536-1276&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1536-1276&client=summon