{"paper_id":"2b20eb53-9f4c-47e5-8d98-27d21f5b4502","body_text":"A robust LU polynomial matrix decomposition for spatial multiplexing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research A robust LU polynomial matrix decomposition for spatial multiplexing Moustapha Mbaye, Moussa Diallo, Mamadou Mboup This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-50211/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Nov, 2020 Read the published version in EURASIP Journal on Advances in Signal Processing → Version 1 posted 12 You are reading this latest preprint version Abstract This paper considers time-domain spatial multiplexing in MIMO wideband system, using an LU-based polynomial matrix decomposition. Because the corresponding pre- and post-filters are not paraunitary, the noise output power is amplified and the performance of the system is degraded, compared to QR-based spatial multiplexing approach. Degradations are important as the post-filter polynomial matrix is ill-conditioned. In this paper, we introduce simple transformations on the decomposition that solve the ill-conditioning problem. We show that this results in a MIMO spatial multiplexing scheme that is robust to noise and channel estimation errors. In the latter context, the proposed LU-based beamforming compares favorably to the QR-based counterpart in terms of complexity and bit error rate. Electrical Engineering Polynomial matrix decomposition LU decomposition Beamforming MIMO wideband system time-domain spatial multiplexing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF. Cite Share Download PDF Status: Published Journal Publication published 10 Nov, 2020 Read the published version in EURASIP Journal on Advances in Signal Processing → Version 1 posted Editorial decision: Minor revision 23 Sep, 2020 Review # 2 received at journal 22 Sep, 2020 Review # 1 received at journal 11 Sep, 2020 Reviewer # 4 agreed at journal 17 Aug, 2020 Reviewer # 1 agreed at journal 14 Aug, 2020 Reviewer # 2 agreed at journal 14 Aug, 2020 Reviewer # 3 agreed at journal 14 Aug, 2020 Reviewers invited by journal 03 Aug, 2020 Editor assigned by journal 30 Jul, 2020 First submitted to journal 29 Jul, 2020 Submission checks completed at journal 29 Jul, 2020 Editor invited by journal 29 Jul, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-50211\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research\",\"associatedPublications\":[],\"authors\":[{\"id\":1109101,\"identity\":\"6a4ddc89-b683-4259-b34a-6d256f7bd419\",\"order_by\":0,\"name\":\"Moustapha Mbaye\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universite Cheikh Anta Diop\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Moustapha\",\"middleName\":\"\",\"lastName\":\"Mbaye\",\"suffix\":\"\"},{\"id\":1109102,\"identity\":\"6fea7cc2-6575-454d-9dda-1fa0814c4a93\",\"order_by\":1,\"name\":\"Moussa 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However, the latest manuscript can be downloaded and \\u003ca href='/article/rs-50211/latest.pdf' target='_blank'\\u003e accessed as a PDF.\\u003c/a\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"eurasip-journal-on-advances-in-signal-processing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jsig\",\"sideBox\":\"Learn more about [EURASIP Journal on Advances in Signal Processing](https://asp-eurasipjournals.springeropen.com/)\",\"snPcode\":\"13634\",\"submissionUrl\":\"https://submission.nature.com/new-submission/13634/3\",\"title\":\"EURASIP Journal on Advances in Signal Processing\",\"twitterHandle\":\"@SpringerEng\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Polynomial matrix decomposition, LU decomposition, Beamforming, MIMO wideband system, time-domain spatial multiplexing\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-50211/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-50211/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"This paper considers time-domain spatial multiplexing in MIMO wideband system, using an LU-based polynomial matrix decomposition. Because the corresponding pre- and post-filters are not paraunitary, the noise output power is amplified and the performance of the system is degraded, compared to QR-based spatial multiplexing approach. Degradations are important as the post-filter polynomial matrix is ill-conditioned. In this paper, we introduce simple transformations on the decomposition that solve the ill-conditioning problem. We show that this results in a MIMO spatial multiplexing scheme that is robust to noise and channel estimation errors. 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