Low‐complexity linear massive MIMO detection based on the improved BFGS method
Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output (MIMO) systems. To avoid the high‐dimensional matrix inversion involved, MMSE detection can be transformed into an unconstrained optimization prob...
Uloženo v:
| Vydáno v: | IET communications Ročník 16; číslo 14; s. 1699 - 1707 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Stevenage
John Wiley & Sons, Inc
01.08.2022
|
| Témata: | |
| ISSN: | 1751-8628, 1751-8636 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output (MIMO) systems. To avoid the high‐dimensional matrix inversion involved, MMSE detection can be transformed into an unconstrained optimization problem and then solved by efficient numerical algorithms in an iterative way. Three low‐complexity Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) quasi‐Newton methods are proposed to iteratively realize massive MIMO MMSE detection without matrix inversion. The complexity can be reduced from O(K3)$\mathcal {O}(K^{3})$ to O(LK2)$\mathcal {O}(LK^{2})$, where K and L denote the number of users and iterations, respectively. Leveraging the special properties of massive MIMO, the authors first explore a simplified BFGS method (named S‐BFGS) to alleviate the computational burden in the search direction. For lower complexity, BFGS method with the unit step size (named U‐BFGS) is presented subsequently. When the base station (BS)‐to‐user‐antenna ratio (BUAR) is large enough, the two proposed BFGS methods can be integrated (named U‐S‐BFGS) to further reduce complexity. In addition, an efficient initialization strategy is devised to accelerate convergence. Simulation results verify that the proposed detection scheme can achieve near‐MMSE performance with a small number of iterations L as low as 2 or 3. |
|---|---|
| AbstractList | Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output (MIMO) systems. To avoid the high‐dimensional matrix inversion involved, MMSE detection can be transformed into an unconstrained optimization problem and then solved by efficient numerical algorithms in an iterative way. Three low‐complexity Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) quasi‐Newton methods are proposed to iteratively realize massive MIMO MMSE detection without matrix inversion. The complexity can be reduced from O(K3)$\mathcal {O}(K^{3})$ to O(LK2)$\mathcal {O}(LK^{2})$, where K and L denote the number of users and iterations, respectively. Leveraging the special properties of massive MIMO, the authors first explore a simplified BFGS method (named S‐BFGS) to alleviate the computational burden in the search direction. For lower complexity, BFGS method with the unit step size (named U‐BFGS) is presented subsequently. When the base station (BS)‐to‐user‐antenna ratio (BUAR) is large enough, the two proposed BFGS methods can be integrated (named U‐S‐BFGS) to further reduce complexity. In addition, an efficient initialization strategy is devised to accelerate convergence. Simulation results verify that the proposed detection scheme can achieve near‐MMSE performance with a small number of iterations L as low as 2 or 3. Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output (MIMO) systems. To avoid the high‐dimensional matrix inversion involved, MMSE detection can be transformed into an unconstrained optimization problem and then solved by efficient numerical algorithms in an iterative way. Three low‐complexity Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) quasi‐Newton methods are proposed to iteratively realize massive MIMO MMSE detection without matrix inversion. The complexity can be reduced from to , where K and L denote the number of users and iterations, respectively. Leveraging the special properties of massive MIMO, the authors first explore a simplified BFGS method (named S‐BFGS) to alleviate the computational burden in the search direction. For lower complexity, BFGS method with the unit step size (named U‐BFGS) is presented subsequently. When the base station (BS)‐to‐user‐antenna ratio (BUAR) is large enough, the two proposed BFGS methods can be integrated (named U‐S‐BFGS) to further reduce complexity. In addition, an efficient initialization strategy is devised to accelerate convergence. Simulation results verify that the proposed detection scheme can achieve near‐MMSE performance with a small number of iterations L as low as 2 or 3. |
| Author | Hu, Jianhao Li, Lin |
| Author_xml | – sequence: 1 givenname: Lin orcidid: 0000-0003-4595-9242 surname: Li fullname: Li, Lin organization: Qinghai Normal University – sequence: 2 givenname: Jianhao surname: Hu fullname: Hu, Jianhao email: jhhu@uestc.edu.cn organization: University of Electronic Science and Technology of China |
| BookMark | eNp9kMtOAjEUhhuDiYBufIIm7kzAttMWulQiSALBRFk3bacTSuaCbQHZ-Qg-o0_i4BgXxrg65-R8_7n8HdAqq9ICcIlRHyMqbkyxJX1MKBYnoI0HDPeGPOGtn5wMz0AnhDVCjHFK2-BxVu0_3t5NVWxy--riAeautMrDQoXgdhbOp_MFTG20JrqqhFoFm8I6iSsLXbHx1a6u78aTJ1jYuKrSc3CaqTzYi-_YBcvx_fPooTdbTKaj21nPUEpEz1jMGDGaM57xVGOOqWZEa40oxcwONBNKUEt1NlBpVjeHSithCElSqjTFSRdcNXPrE162NkS5rra-rFfKBAlCBKYc1RRqKOOrELzNpHFRHT-JXrlcYiSPvsmjb_LLt1py_Uuy8a5Q_vA3jBt473J7-IeUo_mSNJpP6k6AXw |
| CitedBy_id | crossref_primary_10_1109_LCOMM_2024_3363234 crossref_primary_10_1109_ACCESS_2023_3242210 |
| Cites_doi | 10.1137/1019005 10.1109/TCSII.2018.2801867 10.1109/TWC.2010.092810.091092 10.1090/S0025-5718-1967-0224273-2 10.1109/COMST.2019.2935810 10.1109/ICC.2015.7248580 10.1007/s10589-017-9940-7 10.1109/ICC.2017.7996693 10.1109/COMST.2015.2475242 10.1109/ACCESS.2020.2979371 10.1023/B:COAP.0000044184.25410.39 10.1109/MSP.2011.2178495 10.1109/MCOM.2014.6736761 10.1109/TSP.2017.2698410 10.1109/LCOMM.2021.3121445 10.1109/WCSP.2017.8170902 10.1109/LCOMM.2019.2897798 10.1109/ACCESS.2021.3065923 10.1109/TSP.2020.2964234 10.1109/ICDSP.2015.7251869 10.1109/TCSI.2020.2966318 |
| ContentType | Journal Article |
| Copyright | 2022 The Authors. published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. 2022. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2022 The Authors. published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. – notice: 2022. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | 24P AAYXX CITATION 8FE 8FG ABJCF AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS S0W |
| DOI | 10.1049/cmu2.12419 |
| DatabaseName | Wiley Online Library Open Access CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central ProQuest Technology Collection ProQuest One Community College ProQuest Central Korea SciTech Premium Collection ProQuest Engineering Collection Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection DELNET Engineering & Technology Collection |
| DatabaseTitle | CrossRef Advanced Technologies & Aerospace Collection Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest DELNET Engineering and Technology Collection Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection |
| DatabaseTitleList | CrossRef Advanced Technologies & Aerospace Collection |
| Database_xml | – sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1751-8636 |
| EndPage | 1707 |
| ExternalDocumentID | 10_1049_cmu2_12419 CMU212419 |
| Genre | article |
| GroupedDBID | .DC 0R~ 0ZK 1OC 24P 29I 2QL 4.4 4IJ 5GY 6IK 6OB 8FE 8FG 8VB 96U AAHHS AAHJG AAJGR ABJCF ABQXS ACCFJ ACCMX ACESK ACGFO ACGFS ACIWK ACXQS ADEYR AEEZP AEGXH AENEX AEQDE AFAZI AFKRA AIAGR AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN ARAPS AVUZU BENPR BGLVJ CCPQU CS3 DU5 EBS EJD ESX F8P GOZPB GROUPED_DOAJ GRPMH HCIFZ HZ~ IAO IFIPE IPLJI ITC JAVBF K1G L6V LAI M43 M7S MCNEO MS~ NADUK NXXTH O9- OCL OK1 P62 PTHSS QWB RIE RNS ROL RUI S0W U5U UNMZH ZL0 ~ZZ AAMMB AAYXX AEFGJ AFFHD AGXDD AIDQK AIDYY CITATION IDLOA PHGZM PHGZT PQGLB WIN DWQXO PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c4429-ce1552cb656f6db1614b52bbb04415e7b59a94e4bf7adf14b8aba9c223d4ab413 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000801844700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1751-8628 |
| IngestDate | Wed Aug 13 05:29:06 EDT 2025 Wed Oct 29 21:10:04 EDT 2025 Tue Nov 18 22:29:58 EST 2025 Wed Jan 22 16:20:42 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 14 |
| Language | English |
| License | Attribution-NonCommercial-NoDerivs |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4429-ce1552cb656f6db1614b52bbb04415e7b59a94e4bf7adf14b8aba9c223d4ab413 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-4595-9242 |
| OpenAccessLink | https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fcmu2.12419 |
| PQID | 3092291460 |
| PQPubID | 1936360 |
| PageCount | 9 |
| ParticipantIDs | proquest_journals_3092291460 crossref_citationtrail_10_1049_cmu2_12419 crossref_primary_10_1049_cmu2_12419 wiley_primary_10_1049_cmu2_12419_CMU212419 |
| PublicationCentury | 2000 |
| PublicationDate | August 2022 |
| PublicationDateYYYYMMDD | 2022-08-01 |
| PublicationDate_xml | – month: 08 year: 2022 text: August 2022 |
| PublicationDecade | 2020 |
| PublicationPlace | Stevenage |
| PublicationPlace_xml | – name: Stevenage |
| PublicationTitle | IET communications |
| PublicationYear | 2022 |
| Publisher | John Wiley & Sons, Inc |
| Publisher_xml | – name: John Wiley & Sons, Inc |
| References | 2021; 9 2020; 8 2021; 26 2004; 29 1967; 21 2017; 17 2019; 21 1977; 19 2019; 23 2017; 65 2013; 30 1996 2017 2006; 175 2020; 68 2015 2020; 67 2014; 52 2018; 65 2010; 9 2018; 69 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_24_1 e_1_2_8_14_1 e_1_2_8_15_1 Wei Z. (e_1_2_8_16_1) 2006; 175 e_1_2_8_3_1 Dennis J.E. (e_1_2_8_22_1) 1996 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_10_1 e_1_2_8_21_1 e_1_2_8_11_1 e_1_2_8_12_1 e_1_2_8_23_1 |
| References_xml | – volume: 9 start-page: 45740 year: 2021 end-page: 45753 article-title: Low complexity linear detectors for massive MIMO: a comparative study publication-title: IEEE Access – volume: 19 start-page: 46 year: 1977 end-page: 89 article-title: Quasi‐newton methods, motivation and theory publication-title: Siam. Rev. – volume: 65 start-page: 1194 year: 2018 end-page: 1198 article-title: An improved Gauss‐Seidel algorithm and its efficient architecture for massive MIMO systems publication-title: IEEE Trans. Circuits Syst. II Express Briefs – year: 1996 article-title: Numerical methods for unconstrained optimization and nonlinear equations publication-title: SIAM Classics in Applied Mathematics. – volume: 8 start-page: 50244 year: 2020 end-page: 50255 article-title: Fast converging iterative precoding for massive MIMO systems: an accelerated weighted Neumann series‐steepest descent approach publication-title: IEEE Access – volume: 68 start-page: 573 year: 2020 end-page: 588 article-title: Energy‐and area‐efficient recursive‐conjugate‐gradient‐based MMSE detector for massive MIMO systems publication-title: IEEE Trans. Signal Process. – volume: 52 start-page: 186 year: 2014 end-page: 195 article-title: Massive MIMO for next generation wireless systems publication-title: IEEE Commun. Mag. – volume: 67 start-page: 2128 year: 2020 end-page: 2139 article-title: Efficient successive over relaxation detectors for massive MIMO publication-title: IEEE Trans. Circuits Syst. I Regul. Paper – start-page: 1 year: 2017 end-page: 6 – start-page: 248 year: 2015 end-page: 252 – start-page: 1 year: 2017 end-page: 5 – volume: 26 start-page: 138 issue: 1 year: 2021 end-page: 142 article-title: An efficient linear detection scheme based on L‐BFGS method for massive MIMO systems publication-title: IEEE Commun. Lett. – volume: 29 start-page: 315 year: 2004 end-page: 332 article-title: The superlinear convergence of a modified BFGS‐type method for unconstrained optimization publication-title: Comput. Optim. Appl. – volume: 175 start-page: 1156 issue: 2 year: 2006 end-page: 1188 article-title: New quasi‐Newton methods for unconstrained optimization problems publication-title: Appl. Math. Comput. – volume: 21 start-page: 368 year: 1967 end-page: 381 article-title: Quasi‐newton methods and their application to function minimisation publication-title: Math. Compu. – volume: 65 start-page: 3775 year: 2017 end-page: 3788 article-title: Low‐computing‐load, high‐parallelism detection method based on Chebyshev iteration for massive MIMO systems with VLSI architecture publication-title: IEEE Trans. Signal Process – volume: 30 start-page: 40 year: 2013 end-page: 60 article-title: Scaling up MIMO: Opportunities and challenges with very large arrays publication-title: IEEE Signal Process Mag. – volume: 69 start-page: 225 year: 2018 end-page: 241 article-title: On exact linesearch quasi‐Newton methods for minimizing a quadratic function publication-title: Comput. Optim. Appl. – volume: 21 start-page: 3109 year: 2019 end-page: 3132 article-title: Massive MIMO detection techniques: a survey publication-title: IEEE Commun. Surv. Tutor. – volume: 17 start-page: 1941 year: 2017 end-page: 1988 article-title: Fifty years of MIMO detection: the road to large‐scale MIMOs publication-title: IEEE Commun. Surv. Tutor. – start-page: 1763 year: 2015 end-page: 1769 – volume: 23 start-page: 748 year: 2019 end-page: 751 article-title: A low complexity signal detection scheme based on improved Newton iteration for massive MIMO systems publication-title: IEEE Commun. Lett. – volume: 9 start-page: 3590 year: 2010 end-page: 3600 article-title: Noncooperative cellular wireless with unlimited numbers of base station antennas publication-title: IEEE Trans. Wirel. Commun. – ident: e_1_2_8_23_1 doi: 10.1137/1019005 – year: 1996 ident: e_1_2_8_22_1 article-title: Numerical methods for unconstrained optimization and nonlinear equations publication-title: SIAM Classics in Applied Mathematics. – ident: e_1_2_8_11_1 doi: 10.1109/TCSII.2018.2801867 – ident: e_1_2_8_3_1 doi: 10.1109/TWC.2010.092810.091092 – ident: e_1_2_8_19_1 doi: 10.1090/S0025-5718-1967-0224273-2 – ident: e_1_2_8_5_1 doi: 10.1109/COMST.2019.2935810 – ident: e_1_2_8_7_1 doi: 10.1109/ICC.2015.7248580 – ident: e_1_2_8_21_1 doi: 10.1007/s10589-017-9940-7 – ident: e_1_2_8_10_1 doi: 10.1109/ICC.2017.7996693 – ident: e_1_2_8_4_1 doi: 10.1109/COMST.2015.2475242 – ident: e_1_2_8_14_1 doi: 10.1109/ACCESS.2020.2979371 – ident: e_1_2_8_20_1 doi: 10.1023/B:COAP.0000044184.25410.39 – ident: e_1_2_8_24_1 doi: 10.1109/MSP.2011.2178495 – volume: 175 start-page: 1156 issue: 2 year: 2006 ident: e_1_2_8_16_1 article-title: New quasi‐Newton methods for unconstrained optimization problems publication-title: Appl. Math. Comput. – ident: e_1_2_8_2_1 doi: 10.1109/MCOM.2014.6736761 – ident: e_1_2_8_9_1 doi: 10.1109/TSP.2017.2698410 – ident: e_1_2_8_17_1 doi: 10.1109/LCOMM.2021.3121445 – ident: e_1_2_8_13_1 doi: 10.1109/WCSP.2017.8170902 – ident: e_1_2_8_8_1 doi: 10.1109/LCOMM.2019.2897798 – ident: e_1_2_8_6_1 doi: 10.1109/ACCESS.2021.3065923 – ident: e_1_2_8_15_1 doi: 10.1109/TSP.2020.2964234 – ident: e_1_2_8_18_1 doi: 10.1109/ICDSP.2015.7251869 – ident: e_1_2_8_12_1 doi: 10.1109/TCSI.2020.2966318 |
| SSID | ssj0055644 |
| Score | 2.317161 |
| Snippet | Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1699 |
| SubjectTerms | Algorithms Approximation Complexity Error detection MIMO communication Newton methods Optimization Random variables |
| SummonAdditionalLinks | – databaseName: Engineering Database dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3LSgMxFA1aXejCt1itEtCNQrTNJJnJSrRYFdpaqIXuhkkmAwX7sA916Sf4jX6JN-mM7UK6cZdhQhhyk3tOcu-ci9CZpyM4dwSCMMM9wjxGiWQ8JqqkldCAub5y6vpVv14P2m3ZSC_cRmlaZeYTnaOO-9rekV95RUmphH1dvB68Els1ykZX0xIay2jFqiSUXOpeM_PEnAtXzBUQskSAuQeZPCmTV7o7oZeAbVZhZx6QZixznqs6sKls_vczt9BGSjPxzXRdbKMl09tB63Pig7uoUe2_f39-uZxy8wFkHFvGGQ1xF_g0-EBce6w94diMXbJWD1u8izE0gDLijruLgOfbyn0TT8tQ76FW5e65_EDS-gpEM4Ahoo3VXwObcJGIWAH3Y4pTpVTRHrKMr7iMJDNMJX4UJ_AyiFQkNRCKmEUK0G8f5Xr9njlAWAItMCJRlBsbS02kF4ggkTGQFRp5QuTReTbJoU7Fx20NjJfQBcGZDK1BQmeQPDr97TuYSm782auQGSBMt90onM1-Hl04-y0YISzXWtS1DhePdYTWqP3pwaX9FVBuPJyYY7Sq38ad0fDELbkfX3fdcw priority: 102 providerName: ProQuest |
| Title | Low‐complexity linear massive MIMO detection based on the improved BFGS method |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fcmu2.12419 https://www.proquest.com/docview/3092291460 |
| Volume | 16 |
| WOSCitedRecordID | wos000801844700001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1751-8636 dateEnd: 20241231 omitProxy: false ssIdentifier: ssj0055644 issn: 1751-8628 databaseCode: DOA dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1751-8636 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055644 issn: 1751-8628 databaseCode: P5Z dateStart: 20210101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1751-8636 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055644 issn: 1751-8628 databaseCode: M7S dateStart: 20210101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1751-8636 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055644 issn: 1751-8628 databaseCode: BENPR dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library customDbUrl: eissn: 1751-8636 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055644 issn: 1751-8628 databaseCode: WIN dateStart: 20130101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1751-8636 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055644 issn: 1751-8628 databaseCode: 24P dateStart: 20130101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFA6y-eKDd3E6R0BfFKpbmqQN-OKG04GbxSkOX0qSpjDQKbuoj_4Ef6O_xJN09QIiiC8lpemFnOR830nS7yC042sJcUfIPWqY71GfEk9QlniqphXXgLmBcur6Z0GnE_Z6IppBh_m_MJk-xMeEmx0Zzl_bAS5VloUESC0YUd9NyD6gk9X8LNZqfmD7NKFR7ocZ4y6VK-BjzQPeHubipFQcfN77HY4-OeZXpuqgprnwv49cRPNTiomPsj6xhGbMYBnNfREeXEHR2f3T28ur209unoGIY_sqOcR3wKXB_-F2q32OEzN2G7UG2GJdgqEAdBH33TwEnNebJ12cpaBeRVfN48vGqTfNreBpChDkaWO118AejKc8UcD7qGJEKVW1AZYJFBNSUENVGsgkhYuhVFJoIBMJlQqQbw0VBvcDs46wAEpgeKoIM3YdNRV-yMNUJEBUiPQ5L6HdvIljPRUet_kvbmO3AE5FbFspdq1UQtsfdR8yuY0fa5VzS8XTITeK_aogRIDjr5bQnrPJL0-IG-0r4kobf6m8acN6O_9id-6UUWE8nJgtNKsfx_3RsOK6XwUV68ed6KLiovuK3UvahWPEbuB43eq8AxHA4Tk |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3JTsMwEB2xScCBHVFWS8ABJEPrOG58QIitUNEWJKjELcSOI1WCFtqy3fgEvoSP4ksYuw30gLhx4OYoIyvJTN68scczAGuejjDuCATlxvco9zijkvsxVTmthEafm1euun4pX6kEV1fyvA_e07MwNq0yxUQH1HFD2zXybS8rGZP4X2d37-6p7Rpld1fTFhodszg1L08YsrV2ioeo33XGCkeXBye021WAao7gS7WxVcfwSXyRiFgh4-HKZ0qprA0tTF75MpLccJXkozjBm0GkIqnRjcY8Uoj5OG8_DKIwky5V8CJFft8XrnkseuQcxUghSMuhcrmtbx_YFvpSW9Gn1wF-s9pebuycW2H8v32WCRjr0miy17H7Segz9SkY7SmuOA3npcbTx-uby5k3zxhsEMuooya5xXgBMZ6Ui-UzEpu2S0arE-vPY4IDpMSk5tZa8Hq_cHxBOm22Z6D6J680CwP1Rt3MAZFIe4xIFPON3StOpBeIIJExkjEWeUJkYCNVaqi7xdVtj4-b0G3ycxlaAwidAWRg9Uv2rlNS5EepxVThYRdWWuG3tjOw6ezllxnCg3KVudH873OtwPDJZbkUloqV0wUYYfaAh0txXISBdvPBLMGQfmzXWs1lZ-4Erv_akD4BIVg7Wg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbhMxEB6VgBAcoEArAi21BBxAcpN6be_6UFWlIW2UHyJBpKiX7drrlSqRpCQphRuP0Ofp4_RJOnZ2SQ5Vbzn05tVa1q5nPN839ngG4ENgEvQ7Ikm5FQHlAWdUcZFSvWO0NIi5ofbZ9VthpxP1-6q7AlfFXRgXVlnYRG-o05Fxe-SVoKoYU7iuq5UsD4vo1up7Z7-oqyDlTlqLchozFWnavxfovk12GzWU9UfG6l9_HBzRvMIANRwNMTXWZSDDrxIyk6lG9sO1YFrrqnMzbKiFShS3XGdhkmb4Mkp0ogxCasoTjfYfx30AD0P0MV04YVccFygghPSFZBGddyh6DVGRGpWrihmcs23EVZfdZxEM5wx3kSd7oKs_v89TtArPcnpN9mfr4QWs2OFLeLqQdPEVdFuji-t_lz6W3v5BJ4Q4pp2MyQD9CLT9pN1ofyOpnfogtSFxOJ8SbCBVJqd-Dwafv9QPv5NZ-e016C3ll9ahNBwN7WsgCumQlZlmwroz5EwFkYwylSJJY0kgZRk-FQKOTZ503dX--Bn7w3-uYqcMsVeGMrz_3_dslmrk1l4bhfDj3NxM4rnky_DZ684dI8QH7R7zrTd3j7UFj1F_4laj03wLT5i79-EjHzegNB2f2014ZH5PTyfjd17zCZwsW49uABK7RDM |
| 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=Low%E2%80%90complexity+linear+massive+MIMO+detection+based+on+the+improved+BFGS+method&rft.jtitle=IET+communications&rft.au=Li%2C+Lin&rft.au=Hu%2C+Jianhao&rft.date=2022-08-01&rft.issn=1751-8628&rft.eissn=1751-8636&rft.volume=16&rft.issue=14&rft.spage=1699&rft.epage=1707&rft_id=info:doi/10.1049%2Fcmu2.12419&rft.externalDBID=n%2Fa&rft.externalDocID=10_1049_cmu2_12419 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1751-8628&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1751-8628&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1751-8628&client=summon |