General Recursive Least Square Algorithm for Distributed Detection in Massive MIMO

In this paper, a general recursive least square (GRLS) detection algorithm is proposed for the uplink of distributed massive multiple-input multiple-output (MIMO) to alleviate the bottlenecks in both computational complexity and data bandwidth for interconnection. Different from the existing recursi...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on vehicular technology Vol. 73; no. 8; pp. 12137 - 12142
Main Authors Chen, Qiqiang, Wang, Zheng, Ma, Cong, Dai, Xiaoming, Ng, Derrick Wing Kwan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text
ISSN0018-9545
1939-9359
DOI10.1109/TVT.2024.3370611

Cover

Abstract In this paper, a general recursive least square (GRLS) detection algorithm is proposed for the uplink of distributed massive multiple-input multiple-output (MIMO) to alleviate the bottlenecks in both computational complexity and data bandwidth for interconnection. Different from the existing recursive least square (RLS) detection algorithm which only supports a single antenna in each distributed unit (DU), the proposed GRLS allows for multiple antennas in each DU, rendering it adaptable to a variety of practical scenarios. Moreover, among the total <inline-formula><tex-math notation="LaTeX">C</tex-math></inline-formula> DUs and with an integer parameter <inline-formula><tex-math notation="LaTeX">C_{0}</tex-math></inline-formula>, the computational complexity of <inline-formula><tex-math notation="LaTeX">C-C_{0}</tex-math></inline-formula> DUs in GRLS can be significantly reduced by leveraging the channel hardening property. Through analysis, we demonstrate that the convergence of the GRLS algorithm is guaranteed if <inline-formula><tex-math notation="LaTeX">C_{0} \geq \lfloor {(\sqrt{{{B}}/{2}}+\sqrt{K})^{2}}/{B} \rfloor</tex-math></inline-formula> holds, where <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">B</tex-math></inline-formula> denote the numbers of antennas at the user side and each DU, respectively. Furthermore, based on the daisy-chain architecture, the proposed GRLS algorithm also enjoys excellent scalability, which can be easily extended with extra DUs for further improvement. Finally, the detection complexity and data bandwidth analysis are also provided to unveil the superiority of GRLS compared to other distributed detection schemes for massive MIMO.
AbstractList In this paper, a general recursive least square (GRLS) detection algorithm is proposed for the uplink of distributed massive multiple-input multiple-output (MIMO) to alleviate the bottlenecks in both computational complexity and data bandwidth for interconnection. Different from the existing recursive least square (RLS) detection algorithm which only supports a single antenna in each distributed unit (DU), the proposed GRLS allows for multiple antennas in each DU, rendering it adaptable to a variety of practical scenarios. Moreover, among the total [Formula Omitted] DUs and with an integer parameter [Formula Omitted], the computational complexity of [Formula Omitted] DUs in GRLS can be significantly reduced by leveraging the channel hardening property. Through analysis, we demonstrate that the convergence of the GRLS algorithm is guaranteed if [Formula Omitted] holds, where [Formula Omitted] and [Formula Omitted] denote the numbers of antennas at the user side and each DU, respectively. Furthermore, based on the daisy-chain architecture, the proposed GRLS algorithm also enjoys excellent scalability, which can be easily extended with extra DUs for further improvement. Finally, the detection complexity and data bandwidth analysis are also provided to unveil the superiority of GRLS compared to other distributed detection schemes for massive MIMO.
In this paper, a general recursive least square (GRLS) detection algorithm is proposed for the uplink of distributed massive multiple-input multiple-output (MIMO) to alleviate the bottlenecks in both computational complexity and data bandwidth for interconnection. Different from the existing recursive least square (RLS) detection algorithm which only supports a single antenna in each distributed unit (DU), the proposed GRLS allows for multiple antennas in each DU, rendering it adaptable to a variety of practical scenarios. Moreover, among the total <inline-formula><tex-math notation="LaTeX">C</tex-math></inline-formula> DUs and with an integer parameter <inline-formula><tex-math notation="LaTeX">C_{0}</tex-math></inline-formula>, the computational complexity of <inline-formula><tex-math notation="LaTeX">C-C_{0}</tex-math></inline-formula> DUs in GRLS can be significantly reduced by leveraging the channel hardening property. Through analysis, we demonstrate that the convergence of the GRLS algorithm is guaranteed if <inline-formula><tex-math notation="LaTeX">C_{0} \geq \lfloor {(\sqrt{{{B}}/{2}}+\sqrt{K})^{2}}/{B} \rfloor</tex-math></inline-formula> holds, where <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">B</tex-math></inline-formula> denote the numbers of antennas at the user side and each DU, respectively. Furthermore, based on the daisy-chain architecture, the proposed GRLS algorithm also enjoys excellent scalability, which can be easily extended with extra DUs for further improvement. Finally, the detection complexity and data bandwidth analysis are also provided to unveil the superiority of GRLS compared to other distributed detection schemes for massive MIMO.
Author Ma, Cong
Ng, Derrick Wing Kwan
Wang, Zheng
Chen, Qiqiang
Dai, Xiaoming
Author_xml – sequence: 1
  givenname: Qiqiang
  orcidid: 0009-0005-2654-3331
  surname: Chen
  fullname: Chen, Qiqiang
  organization: School of Information Science and Engineering, Southeast University, Nanjing, China
– sequence: 2
  givenname: Zheng
  orcidid: 0000-0003-3528-558X
  surname: Wang
  fullname: Wang, Zheng
  email: wznuaa@gmail.com
  organization: School of Information Science and Engineering, Southeast University, Nanjing, China
– sequence: 3
  givenname: Cong
  orcidid: 0000-0001-7631-1970
  surname: Ma
  fullname: Ma, Cong
  email: 1192403915@qq.com
  organization: ZTE Corporation, Shenzhen, China
– sequence: 4
  givenname: Xiaoming
  orcidid: 0000-0001-5817-8723
  surname: Dai
  fullname: Dai, Xiaoming
  email: daixiaoming@ustb.edu.cn
  organization: University of Science and Technology Beijing, Beijing, China
– sequence: 5
  givenname: Derrick Wing Kwan
  orcidid: 0000-0001-6400-712X
  surname: Ng
  fullname: Ng, Derrick Wing Kwan
  email: w.k.ng@unsw.edu.au
  organization: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia
BookMark eNp9kM1PAjEQxRuDiYDePXho4nmxX_vRIwFFEggJotemuzurJcsutF0T_3uLcDAePE0meb95b94A9Zq2AYRuKRlRSuTD5m0zYoSJEecpSSi9QH0quYwkj2UP9QmhWSRjEV-hgXPbsAohaR-tZ9CA1TVeQ9FZZz4BL0A7j18OnbaAx_V7a43_2OGqtXhqnLcm7zyUeAoeCm_aBpsGL7X7YZfz5eoaXVa6dnBznkP0-vS4mTxHi9VsPhkvooJJ5kMYBmWlM6bzpGB5SXmWlyUVDFJSCskkz6FkTMdpThhALAHSClJeiCxNWAZ8iO5Pd_e2PXTgvNq2nW2CpeJEipjyJCVBRU6qwrbOWajU3pqdtl-KEnVsToXm1LE5dW4uIMkfpDBeH1_1Vpv6P_DuBBoA-OUjQo5E8m-LNX02
CODEN ITVTAB
CitedBy_id crossref_primary_10_4218_etrij_2024_0190
Cites_doi 10.1063/1.3034123
10.1109/TSP.2019.2928947
10.1109/TSP.2018.2831622
10.1109/TVT.2020.3008151
10.1109/COMST.2019.2935810
10.1109/TVT.2021.3133111
10.1109/TWC.2021.3110839
10.1109/TCSI.2021.3097042
10.1109/TSP.2020.2964496
10.1109/SiPS.2018.8598321
10.1109/mwc.132.2200443
10.1109/MSP.2011.2178495
10.1109/JETCAS.2017.2775151
10.1109/ACSSC.2016.7869083
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
DBID 97E
RIA
RIE
AAYXX
CITATION
7SP
8FD
FR3
KR7
L7M
DOI 10.1109/TVT.2024.3370611
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Xplore digital library
CrossRef
Electronics & Communications Abstracts
Technology Research Database
Engineering Research Database
Civil Engineering Abstracts
Advanced Technologies Database with Aerospace
DatabaseTitle CrossRef
Civil Engineering Abstracts
Engineering Research Database
Technology Research Database
Advanced Technologies Database with Aerospace
Electronics & Communications Abstracts
DatabaseTitleList Civil Engineering Abstracts

Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library (IEL)
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1939-9359
EndPage 12142
ExternalDocumentID 10_1109_TVT_2024_3370611
10470369
Genre orig-research
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: 62371124; 62371037
  funderid: 10.13039/501100001809
– fundername: ZTE Industry-University-Institute Cooperation Funds
  grantid: HC-CN-20200628010
GroupedDBID -~X
.DC
0R~
29I
3EH
4.4
5GY
5VS
6IK
97E
AAIKC
AAJGR
AAMNW
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACGFO
ACGFS
ACIWK
ACNCT
AENEX
AETIX
AGQYO
AGSQL
AHBIQ
AI.
AIBXA
AKJIK
AKQYR
ALLEH
ALMA_UNASSIGNED_HOLDINGS
ASUFR
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
HZ~
H~9
IAAWW
IBMZZ
ICLAB
IFIPE
IFJZH
IPLJI
JAVBF
LAI
M43
MS~
O9-
OCL
P2P
RIA
RIE
RNS
RXW
TAE
TN5
VH1
AAYXX
CITATION
7SP
8FD
FR3
KR7
L7M
ID FETCH-LOGICAL-c292t-952edfa82ab6c2bd138bdd142e70d49293bed22a57b02ee59ee7fe73c487628e3
IEDL.DBID RIE
ISSN 0018-9545
IngestDate Mon Jun 30 10:12:42 EDT 2025
Wed Oct 01 02:27:17 EDT 2025
Thu Apr 24 22:54:51 EDT 2025
Wed Aug 27 02:32:38 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 8
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-c292t-952edfa82ab6c2bd138bdd142e70d49293bed22a57b02ee59ee7fe73c487628e3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0001-7631-1970
0000-0003-3528-558X
0009-0005-2654-3331
0000-0001-6400-712X
0000-0001-5817-8723
PQID 3094513670
PQPubID 85454
PageCount 6
ParticipantIDs crossref_primary_10_1109_TVT_2024_3370611
ieee_primary_10470369
proquest_journals_3094513670
crossref_citationtrail_10_1109_TVT_2024_3370611
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2024-08-01
PublicationDateYYYYMMDD 2024-08-01
PublicationDate_xml – month: 08
  year: 2024
  text: 2024-08-01
  day: 01
PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
PublicationTitle IEEE transactions on vehicular technology
PublicationTitleAbbrev TVT
PublicationYear 2024
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 ref13
ref12
ref14
ref11
ref10
ref2
ref1
ref8
ref7
ref9
ref4
ref3
ref6
Krishnamoorthy (ref15) 2013
ref5
References_xml – ident: ref13
  doi: 10.1063/1.3034123
– ident: ref5
  doi: 10.1109/TSP.2019.2928947
– ident: ref12
  doi: 10.1109/TSP.2018.2831622
– ident: ref4
  doi: 10.1109/TVT.2020.3008151
– ident: ref1
  doi: 10.1109/COMST.2019.2935810
– ident: ref6
  doi: 10.1109/TVT.2021.3133111
– ident: ref11
  doi: 10.1109/TWC.2021.3110839
– ident: ref7
  doi: 10.1109/TCSI.2021.3097042
– ident: ref9
  doi: 10.1109/TSP.2020.2964496
– ident: ref8
  doi: 10.1109/SiPS.2018.8598321
– ident: ref10
  doi: 10.1109/mwc.132.2200443
– ident: ref14
  doi: 10.1109/MSP.2011.2178495
– ident: ref3
  doi: 10.1109/JETCAS.2017.2775151
– start-page: 70
  volume-title: Proc. IEEE Signal Process. Algorithms, Architectures, Arrangements, Appl. Conf.
  year: 2013
  ident: ref15
  article-title: Matrix inversion using Cholesky decomposition
– ident: ref2
  doi: 10.1109/ACSSC.2016.7869083
SSID ssj0014491
Score 2.457424
Snippet In this paper, a general recursive least square (GRLS) detection algorithm is proposed for the uplink of distributed massive multiple-input multiple-output...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 12137
SubjectTerms Algorithms
Antenna arrays
Antennas
Bandwidth
Bandwidths
Complexity
Complexity theory
Computer architecture
Daisy-chain
decentralized signal detection
Detection algorithms
distributed MIMO detection
Least squares
Massive MIMO
MIMO communication
RLS
Title General Recursive Least Square Algorithm for Distributed Detection in Massive MIMO
URI https://ieeexplore.ieee.org/document/10470369
https://www.proquest.com/docview/3094513670
Volume 73
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVIEE
  databaseName: IEEE/IET Electronic Library (IEL)
  customDbUrl:
  eissn: 1939-9359
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0014491
  issn: 0018-9545
  databaseCode: RIE
  dateStart: 19670101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3NT8IwFG-Ekx78xIii6cGLh8HWdmw7EpGgEUwQDLdlbd-UiENxXPzr7dsGQY3G25a0TfNe-z763vs9Qs6VlHbTdyMrirS0BATaigRz8NeTsfalirAauddvdkfiZuyOi2L1rBYGALLkM6jjZxbL1zO1wKeyBsIKGIkblEjJ85t5sdYqZCBE0R7PMTfY2AXLmKQdNIYPQ-MJMlHn3DP6y_mig7KmKj8kcaZeOjukv9xYnlXyXF-ksq4-vmE2_nvnu2S7MDRpKz8Ze2QDkn2ytQY_eEAGBeY0HeCjO-ax01ts5UPv38zBAdqaPs7mk_TphRrLlrYRYhe7Y4GmbUizFK6EThLaM_Y3zu1d9-4qZNS5Gl52raLHgqVYwFJDKQY6jnwWyaZiUjvcl1o7goFna2FsJy5BMxa5nrQZgBsAeDF4XAkUoz7wQ1JOZgkcEQoyNt6HrWKpYoG4fWAGS2W7MUcYOV4ljSXVQ1UAkGMfjGmYOSJ2EBo-hcinsOBTlVysZrzm4Bt_jK0g2dfG5RSvktqSs2FxPd9DbpxaF8Hq7ONfpp2QTVw9T_WrkXI6X8CpMT9SeZYdu09z1teB
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LT8MwDLZ4HIADb8R45sCFQ7c2Sdf2iHhowDokGIhb1SQuIKDj0V349cRth3gIxK2VEiWyEz9i-zPAjlbKbYd-6qSpUY7EyDip5B79BiozodIpVSPHvXbnUp5c-9d1sXpZC4OIZfIZNumzjOWbgR7SU1mLYAWsxI3GYdKXUvpVudZH0EDKukGeZ--wtQxGUUk3avWv-tYX5LIpRGA1mPdFC5VtVX7I4lLBHM1Bb7S1Kq_kvjksVFO_fUNt_Pfe52G2NjXZXnU2FmAM80WY-QRAuATnNeo0O6dnd8pkZ11q5sMunu3RQbb3cDN4uStuH5m1bdkBgexSfyw07ACLMokrZ3c5i60FTnPj4_hsGS6PDvv7HafusuBoHvHCUoqjydKQp6qtuTKeCJUxnuQYuEZa60koNJynfqBcjuhHiEGGgdCSBGmIYgUm8kGOq8BQZdb_cHWmdCYJuQ_tYKVdPxMEJCca0BpRPdE1BDl1wnhISlfEjRLLp4T4lNR8asDux4ynCn7jj7HLRPZP4yqKN2BjxNmkvqCvibBurU9wde7aL9O2YarTj7tJ97h3ug7TtFKV-LcBE8XLEDetMVKorfIIvgPnm9rO
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=General+Recursive+Least+Square+Algorithm+for+Distributed+Detection+in+Massive+MIMO&rft.jtitle=IEEE+transactions+on+vehicular+technology&rft.au=Chen%2C+Qiqiang&rft.au=Wang%2C+Zheng&rft.au=Ma%2C+Cong&rft.au=Dai%2C+Xiaoming&rft.date=2024-08-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0018-9545&rft.eissn=1939-9359&rft.volume=73&rft.issue=8&rft.spage=12137&rft_id=info:doi/10.1109%2FTVT.2024.3370611&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9545&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9545&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9545&client=summon