Deep Learning-based Uplink Data Detection in User-Centric Cell-Free mMIMO Systems

Deep Learning-based Uplink Data Detection in User-Centric Cell-Free mMIMO 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 Deep Learning-based Uplink Data Detection in User-Centric Cell-Free mMIMO Systems Noorollah Hematidoust, Aliakbar Tadaion, Stefano Buzzi, Maurizio Magarini, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5336882/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Iranian Journal of Science and Technology, Transactions of Electrical Engineering → Version 1 posted 4 You are reading this latest preprint version Abstract This paper tackles the problem of uplink data detection in a user-centric cell-free massive multi-input multi-output (UC-CF-mMIMO). First of all, we show that the problem of uplink data detection in UC-CF-mMIMO with large-scale fading decoding (LSFD) can be cast as a classical MIMO detection problem. Next, we develop a new detection structure, called LMDPIC, which combines linear minimum mean square error (LMMSE) and deep-learning-based parallel interference cancellation (DeepPIC) detectors for symbol detection. Simulation results demonstrate that LMDPIC outperforms other state-of-the-art MIMO detection schemes for BPSK and QPSK modulation schemes, both in the case of the perfect and in that of imperfect channel state information (CSI) available at the APs. We also propose two heuristic pilot assignment schemes to improve the quality of CSI acquisition during uplink training. The performance of LMDPIC is also evaluated for several power-control strategies, including half-power, max-min, and full-power transmission. Our results show that for 5% UEs with the highest pairwise symbol error rate, the LMDPIC with half power transmission outperforms the LMMSE with full power transmission. Finally the paper also shows that the deep neural network (DNN) used in the LMDPIC structure is robust against CSI time variations. User-centric cell-free massive MIMO pilot contamination MIMO detection Deep neural networks 6G Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Iranian Journal of Science and Technology, Transactions of Electrical Engineering → Version 1 posted Editorial decision: Revision requested 08 Nov, 2024 Editor assigned by journal 28 Oct, 2024 Submission checks completed at journal 28 Oct, 2024 First submitted to journal 26 Oct, 2024 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-5336882","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371263098,"identity":"b9f02a95-514d-477f-94dc-abd18f346a86","order_by":0,"name":"Noorollah Hematidoust","email":"","orcid":"","institution":"Yazd University","correspondingAuthor":false,"prefix":"","firstName":"Noorollah","middleName":"","lastName":"Hematidoust","suffix":""},{"id":371263099,"identity":"2de1a214-ea9b-4ff8-873d-b4b9be7e670a","order_by":1,"name":"Aliakbar Tadaion","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYHACA4YEBgY5EEsCRLARUs8D1WJMohYgSGyAaSEI7NkPb/zwoOJO+objxx/eYKixY+CTPkDAFp60YomEM89yN5xJSLZgOJbMwMaXQMhhOQYSiW2HczccSDgmwcB2gIGNh5Bf+N8Y/wBqSTc4/7BNguEfMVokcsxAtiQY3Ehmk2BsI0bLjWdlFglnDhvOvPGM2SKxL5mHoBb2/uTNN39UHJbnO5/+8MaHb3Zy8j0EtMCBwgEgkQCOKGKBfAPxakfBKBgFo2CEAQD3yz9oVa+WmQAAAABJRU5ErkJggg==","orcid":"","institution":"Yazd University","correspondingAuthor":true,"prefix":"","firstName":"Aliakbar","middleName":"","lastName":"Tadaion","suffix":""},{"id":371263100,"identity":"faae7050-698f-4f76-ae99-b82ba682c53c","order_by":2,"name":"Stefano Buzzi","email":"","orcid":"","institution":"Politecnico di Milano","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Buzzi","suffix":""},{"id":371263101,"identity":"5324274b-e8ca-4392-86ab-7f0c13b7a375","order_by":3,"name":"Maurizio Magarini","email":"","orcid":"","institution":"Politecnico di Milano","correspondingAuthor":false,"prefix":"","firstName":"Maurizio","middleName":"","lastName":"Magarini","suffix":""},{"id":371263102,"identity":"d148d478-7d58-4a17-be4b-97e7a66f0c53","order_by":4,"name":"Mohammadtaghi Sadeghi","email":"","orcid":"","institution":"Yazd University","correspondingAuthor":false,"prefix":"","firstName":"Mohammadtaghi","middleName":"","lastName":"Sadeghi","suffix":""}],"badges":[],"createdAt":"2024-10-26 10:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5336882/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5336882/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40998-025-00811-8","type":"published","date":"2025-04-29T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81988289,"identity":"5069f808-ea65-40e1-9daa-8ae3cf2e8b22","added_by":"auto","created_at":"2025-05-05 16:08:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":778380,"visible":true,"origin":"","legend":"","description":"","filename":"DeepPICCellfreemMIMO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5336882/v1_covered_6ba53b2e-a4a1-4ce5-acbc-df7b0ebac336.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning-based Uplink Data Detection in User-Centric Cell-Free mMIMO Systems","fulltext":[],"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":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"iranian-journal-of-science-and-technology-transactions-of-electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iste","sideBox":"Learn more about [Iranian Journal of Science and Technology, Transactions of Electrical Engineering](https://link.springer.com/journal/40998)","snPcode":"40998","submissionUrl":"https://submission.springernature.com/new-submission/40998/3","title":"Iranian Journal of Science and Technology, Transactions of Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"User-centric cell-free massive MIMO, pilot contamination, MIMO detection, Deep neural networks, 6G","lastPublishedDoi":"10.21203/rs.3.rs-5336882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5336882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper tackles the problem of uplink data detection in a user-centric cell-free massive multi-input multi-output (UC-CF-mMIMO). First of all, we show that the problem of uplink data detection in UC-CF-mMIMO with large-scale fading decoding (LSFD) can be cast as a classical MIMO detection problem. Next, we develop a new detection structure, called LMDPIC, which combines linear minimum mean square error (LMMSE) and deep-learning-based parallel interference cancellation (DeepPIC) detectors for symbol detection. Simulation results demonstrate that LMDPIC outperforms other state-of-the-art MIMO detection schemes for BPSK and QPSK modulation schemes, both in the case of the perfect and in that of imperfect channel state information (CSI) available at the APs. We also propose two heuristic pilot assignment schemes to improve the quality of CSI acquisition during uplink training. The performance of LMDPIC is also evaluated for several power-control strategies, including half-power, max-min, and full-power transmission. 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