Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes

Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes | 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 Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes Guangwen Li, Xiao Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8375438/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This paper introduces a hybrid decoding architecture that serially couples a normalized min-sum (NMS) decoder with reinforced ordered statistics decoding (OSD) to achieve near-maximum likelihood (ML) performance for short linear block codes. The framework incorporates several key innovations: a decoding information aggregation model that employs a convolutional neural network to refine bit reliability estimates for OSD using the soft-output trajectory of the NMS decoder; an adaptive decoding path for OSD, initialized by the arranged list of the most a priori likely tests algorithm and dynamically updated with empirical data; and a sliding window assisted model that enables early termination of test error patterns’ traversal, curbing complexity with minimal performance loss. For short high-rate codes, a dedicated undetected error detector identifies erroneous NMS outcomes that satisfy parity checks, ensuring they are forwarded to OSD for correction. Extensive simulations on LDPC, BCH, and RS codes demonstrate that the proposed hybrid decoder delivers a competitive trade-off, achieving near-ML frame error rate performance while maintaining advantages in throughput, latency, and complexity over state-of-the-art alternatives. neural network belief propagation min-sum ordered statistics decoding block codes Full Text Supplementary Files SupplementaryExperimentsforReview.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 05 Mar, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor assigned by journal 18 Dec, 2025 First submitted to journal 17 Dec, 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-8375438","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578771859,"identity":"e25a6345-2faa-4204-80a2-e4bcef7cf0e5","order_by":0,"name":"Guangwen Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACPhBRAcQS7A0MEowNIG4Cfi1sIOIMSAvPAQaJg6RpkUggVgv/GgOGg3vs8iRnPn94++OOwwz87DkGDD934NEi8caA4cCz5GJp6Rxji4NnDjNI9rwxYOw9g0/L2Q3MHw4wJ86TzmGTONh2mMHgRo4BM2Mbfi0MBw7UJ86TPP4MrMWeoBb+XpCWw4mzJRjMILZIELSF/8OBAweOJ87sAfrl7Jl0HokzzwoO9uLRws9/LPHBgQPViTOOH394o3KHtRx/e/LGBz/xaGEARscBZD4PiDiATSXCGvzSo2AUjIJRMAoYGAA8jVfQOu6DnwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7339-9679","institution":"Shandong Technology and Business University","correspondingAuthor":true,"prefix":"","firstName":"Guangwen","middleName":"","lastName":"Li","suffix":""},{"id":578771860,"identity":"ebe6723a-2802-4c66-9cb8-ffc7d7a30235","order_by":1,"name":"Xiao Yu","email":"","orcid":"","institution":"Binzhou Medical University - Yantai Campus","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-12-16 11:20:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8375438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8375438/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101296815,"identity":"73e797c2-0ab9-428b-a2a9-461ec145faa0","added_by":"auto","created_at":"2026-01-28 09:21:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16004519,"visible":true,"origin":"","legend":"","description":"","filename":"main.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8375438/v1_covered_55cc88a3-a652-44ab-838a-d783c99a87a6.pdf"},{"id":101226014,"identity":"3024e5ee-5c99-47bf-a9cf-452e82880f62","added_by":"auto","created_at":"2026-01-27 12:41:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":50220425,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryExperimentsforReview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8375438/v1/a268445e47fb5eae0b93d917.pdf"}],"financialInterests":"","formattedTitle":"Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"eurasip-journal-on-wireless-communications-and-networking","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jwcn","sideBox":"Learn more about [EURASIP Journal on Wireless Communications and Networking](http://jwcn-eurasipjournals.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jwcn/default.aspx","title":"EURASIP Journal on Wireless Communications and Networking","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"neural network, belief propagation, min-sum, ordered statistics decoding, block codes","lastPublishedDoi":"10.21203/rs.3.rs-8375438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8375438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper introduces a hybrid decoding architecture that serially couples a normalized min-sum (NMS) decoder with reinforced ordered statistics decoding (OSD) to achieve near-maximum likelihood (ML) performance for short linear block codes. 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