ST-TNet:An Spatio-temporal Joint Transformer Network for CSI Feedback in FDD-MIMO Systems

ST-TNet:An Spatio-temporal Joint Transformer Network for CSI Feedback in FDD-MIMO Systems | 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 Article ST-TNet:An Spatio-temporal Joint Transformer Network for CSI Feedback in FDD-MIMO Systems Linyu wang, Yize cao, jian hong xiang, Hanyu Jiang, Yu zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4609872/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, deep learning methods have been shown to have strong potential and superiority in reducing channel state information (CSI) feedback overhead and further improving feedback accuracy to maximize the performance benefits of massive MIMO in frequency division duplex (FDD) mode. As the CSI matrices are transformed into sequences for input to the Transformer model, the rearrangement leads to the loss of the original physical location relationships. Based on this problem, this paper proposes a transformer decoder based on spatio-temporal joint (ST-T). We employ a spatial attention mechanism to compensate for this information loss and focus on key spatial features more accurately, further exploiting the potential of single- and two-layer transformers in reconstructing CSI matrices. The results are validated by simulations based on DCRNet and CLNet encoders, which show that higher performance can be achieved with lower computational load compared to other lightweight models. Massive MIMO CSI Feedback Deep Learning CNN Transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4609872","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320677037,"identity":"ea1b2fae-abcc-4ec9-b6bf-d285a420b1e9","order_by":0,"name":"Linyu wang","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Linyu","middleName":"","lastName":"wang","suffix":""},{"id":320677038,"identity":"505e94d6-fa07-4a62-af1c-bfd687c41ba9","order_by":1,"name":"Yize cao","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Yize","middleName":"","lastName":"cao","suffix":""},{"id":320677039,"identity":"fe55921e-4037-4067-bbe7-cf69e67877f1","order_by":2,"name":"jian hong xiang","email":"data:image/png;base64,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","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":true,"prefix":"","firstName":"jian","middleName":"hong","lastName":"xiang","suffix":""},{"id":320677040,"identity":"a01c8cb1-c8d4-4db1-9fa7-a655bdefcc4d","order_by":3,"name":"Hanyu Jiang","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Hanyu","middleName":"","lastName":"Jiang","suffix":""},{"id":320677041,"identity":"852d3898-74bc-4f52-81b3-09e8137f1d6c","order_by":4,"name":"Yu zhong","email":"","orcid":"","institution":"Agile and Intelligent Computing Key Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"zhong","suffix":""}],"badges":[],"createdAt":"2024-06-20 07:21:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4609872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4609872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61250998,"identity":"4503a666-5b8f-4168-b8ff-1443ce2f1333","added_by":"auto","created_at":"2024-07-28 11:05:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":609144,"visible":true,"origin":"","legend":"","description":"","filename":"STTNet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4609872/v1_covered_e0c7a4ee-bd5a-476c-8810-c0da7e5711fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ST-TNet:An Spatio-temporal Joint Transformer Network for CSI Feedback in FDD-MIMO Systems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Massive MIMO,CSI Feedback, Deep Learning, CNN, Transformer","lastPublishedDoi":"10.21203/rs.3.rs-4609872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4609872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, deep learning methods have been shown to have strong potential and superiority in reducing channel state information (CSI) feedback overhead and further improving feedback accuracy to maximize the performance benefits of massive MIMO in frequency division duplex (FDD) mode. 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