Channel Estimation and Interference Management in Multi-User MIMO Systems Using Integrated Deep Learning and Quantum Computing

Channel Estimation and Interference Management in Multi-User MIMO Systems Using Integrated Deep Learning and Quantum Computing | 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 Channel Estimation and Interference Management in Multi-User MIMO Systems Using Integrated Deep Learning and Quantum Computing Shutong Yao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5051284/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 This study presents a novel approach combining deep learning with quantum computing for channel estimation and interference management in multi-user MIMO systems. Addressing the limitations of traditional channel estimation methods in complex wireless communication environments, we introduce quantum computing techniques, including quantum Fourier transforms and quantum state reconstruction algorithms, to enhance the accuracy and efficiency of channel estimation. Building on the results from quantum computing, we further optimize the outcomes using deep learning networks, focusing specifically on interference cancellation and data compression. In the experimental setup, we simulated the channel matrix of a multi-user MIMO system on a quantum computing platform and trained a convolutional neural network (CNN) for deep learning optimization. The findings demonstrate that the integration of quantum computing and deep learning substantially enhances system performance, particularly in managing interference and processing data in complex channel environments. This study not only provides new technical methods for channel estimation and interference management in multi-user communication systems but also paves the way for further research into the combined application of quantum computing and deep learning. Deep Learning Quantum Computing Multi-User MIMO Systems Channel Estimation Interference Management. 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. 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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-5051284","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375955556,"identity":"70f18ad6-2cfc-4332-82b6-d0677655443c","order_by":0,"name":"Shutong Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3RMQuCQBTA8TsOruXC9STwGwQnguCiX0URmmx3yxC8hmt38FsEzgeBLX4AxyJoMrCpJai1Jq8t6P77j/ceDwCd7gczkHEfBva0IM+lGjF55sIyRQ4STahGWCtdRFoU8TJhipt14eI4LTDclsmt64FvzbMRAcvwwMyCIFNcd14FYseVIwTRKKN2QbHNl/WMABnVYwTTGNCoYCSQyUWNENJgKtuQwk2C1QidCGRnqWRQNI5XMYVbgj2B5weTqzXPT12f+tYo+RxJVF/zRr4VOp1O9xe9ACu7RK0VCIRCAAAAAElFTkSuQmCC","orcid":"","institution":"Fuzhou University","correspondingAuthor":true,"prefix":"","firstName":"Shutong","middleName":"","lastName":"Yao","suffix":""}],"badges":[],"createdAt":"2024-09-08 06:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5051284/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5051284/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78701404,"identity":"fcae96ef-7496-489f-9256-afbae42b7733","added_by":"auto","created_at":"2025-03-17 19:16:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":793928,"visible":true,"origin":"","legend":"","description":"","filename":"channelestimationandinterferencemanagement3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5051284/v1_covered_dba47b26-c51c-4362-9663-eff6631c0a05.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Channel Estimation and Interference Management in Multi-User MIMO Systems Using Integrated Deep Learning and Quantum Computing","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":"Deep Learning, Quantum Computing, Multi-User MIMO Systems, Channel Estimation, Interference Management.","lastPublishedDoi":"10.21203/rs.3.rs-5051284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5051284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a novel approach combining deep learning with quantum computing for channel estimation and interference management in multi-user MIMO systems. 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