{"paper_id":"11671589-6958-47ea-9aa6-aeded70817a8","body_text":"Near-field Extremely large-scale MIMO Data Rate Prediction Based on Deep Learning | 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 Near-field Extremely large-scale MIMO Data Rate Prediction Based on Deep Learning Guozhi Rong, Rugui Yao, Yifeng He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5494329/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jul, 2025 Read the published version in Discover Computing → Version 1 posted 9 You are reading this latest preprint version Abstract The channel matrix dimension of the near-field ultra-large-scale MIMO (Multiple-Input Multiple-Output) system is extremely high due to the significant increase in the number of antennas, thus aggravating the computational and storage burdens, and posing challenges to real-time processing and system resource management. Furthermore, traditional methods for obtaining Channel State Information (CSI) may perform poorly in near-field extremely large-scale MIMO systems, making it difficult to accurately capture the channel characteristics, which in turn affect the overall performance of the system. This study utilized the CsiNet-LSTM (Long Short-Term Memory) model to realize the channel capacity prediction.. This method combined the efficient CSI compression technique of CsiNet model with the temporal prediction capability of LSTM network, which could more accurately capture the dynamic characteristics of near-field extremely large-scale MIMO channels, thereby improving the accuracy of channel capacity prediction. During the research process, this article utilized communication simulation tools to generate CSI data under multiple propagation environments and normalize and segment them, then built encoders and decoders for the CsiNet model for extracting and reconstructing CSI features, and finally combined them with the LSTM model for time series modeling. The experimental results showed that the signal strength of the normalized signal strength of CsiNet-LSTM in a multipath propagation environment reaches 0.6, and the signal quality under noise conditions reached 0.7, which was superior to other models and demonstrated stability in complex environments. In terms of real-time performance, CsiNet-LSTM had an average prediction time of 0.35 seconds and a processing speed of 2857 samples per second, demonstrating excellent real-time processing capabilities compared to other models. Ultra Large Scale Multiple-Input Multiple-Output Near-field Environment Channel Capacity Channel State Information Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Jul, 2025 Read the published version in Discover Computing → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 14 May, 2025 Editor assigned by journal 07 May, 2025 Reviews received at journal 06 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers invited by journal 28 Apr, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 23 Apr, 2025 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-5494329\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":449005674,\"identity\":\"34b31a1e-94a4-4555-8403-8502fc973342\",\"order_by\":0,\"name\":\"Guozhi Rong\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Northwestern Polytechnical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Guozhi\",\"middleName\":\"\",\"lastName\":\"Rong\",\"suffix\":\"\"},{\"id\":449005675,\"identity\":\"40400669-5022-43d4-a2da-a4bdbc8a56d5\",\"order_by\":1,\"name\":\"Rugui Yao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Northwestern Polytechnical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rugui\",\"middleName\":\"\",\"lastName\":\"Yao\",\"suffix\":\"\"},{\"id\":449005676,\"identity\":\"43512186-73c8-43f6-90a6-c5bfa54b645c\",\"order_by\":2,\"name\":\"Yifeng He\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Northwestern Polytechnical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yifeng\",\"middleName\":\"\",\"lastName\":\"He\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-11-21 03:23:11\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5494329/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5494329/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s10791-025-09654-7\",\"type\":\"published\",\"date\":\"2025-07-04T15:58:24+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":86179222,\"identity\":\"26cbf135-c441-461b-9ae1-8232fcd932ec\",\"added_by\":\"auto\",\"created_at\":\"2025-07-07 16:17:24\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":970875,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"NearfieldExtremely.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5494329/v1_covered_374c3b51-5336-4b00-99bb-487f436d967e.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Near-field Extremely large-scale MIMO Data Rate Prediction Based on Deep Learning\",\"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\":\"info@researchsquare.com\",\"identity\":\"discover-computing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Discover Computing](https://link.springer.com/journal/10791)\",\"snPcode\":\"10791\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/10791/3\",\"title\":\"Discover Computing\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Discover Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Ultra Large Scale Multiple-Input Multiple-Output, Near-field Environment, Channel Capacity, Channel State Information, Deep Learning\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5494329/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5494329/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe channel matrix dimension of the near-field ultra-large-scale MIMO (Multiple-Input Multiple-Output) system is extremely high due to the significant increase in the number of antennas, thus aggravating the computational and storage burdens, and posing challenges to real-time processing and system resource management. Furthermore, traditional methods for obtaining Channel State Information (CSI) may perform poorly in near-field extremely large-scale MIMO systems, making it difficult to accurately capture the channel characteristics, which in turn affect the overall performance of the system. This study utilized the CsiNet-LSTM (Long Short-Term Memory) model to realize the channel capacity prediction.. This method combined the efficient CSI compression technique of CsiNet model with the temporal prediction capability of LSTM network, which could more accurately capture the dynamic characteristics of near-field extremely large-scale MIMO channels, thereby improving the accuracy of channel capacity prediction. 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