Genetic Algorithm-Optimized Adaptive Offset Min-Sum LDPC Decoder for 5G- NR Base Graph 2 Codes

Genetic Algorithm-Optimized Adaptive Offset Min-Sum LDPC Decoder for 5G- NR Base Graph 2 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 Genetic Algorithm-Optimized Adaptive Offset Min-Sum LDPC Decoder for 5G- NR Base Graph 2 Codes Anwer Sabah Ahmed, Yousif Jawad Kadhim Nukhailawi, Heyam A. Marzog, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8646460/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 paper proposes a resource-efficient Adaptive Offset Min-Sum (A-OMS) LDPC decoder for 5G-NR Base Graph 2 (BG2) codes. A Genetic Algorithm (GA) is employed to optimize the offset factor within the check-node update, addressing the difficulty of selecting a globally optimal offset for the irregular BG2 structure. The GA operates as a meta-heuristic optimizer that minimizes the bit-error rate (BER) over a wide range of signal-to-noise ratios (SNRs). Simulation results show that the proposed GA-optimized A-OMS decoder achieves performance within 0.2–0.4 dB of belief-propagation decoding, while providing coding gains of 0.3–0.6 dB and up to a 4.6× BER reduction compared with conventional fixed-offset OMS. These improvements are obtained with approximately 35% of the computational complexity of BP decoding. The proposed approach offers a practical balance between performance and implementation efficiency for next-generation 5G-NR receivers. Adaptive decoding Base Graph 2 (BG2) 5G-NR LDPC decoding Min-Sum algorithm Offset Min-Sum (OMS) SNR-adaptive offset 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-8646460","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578849521,"identity":"98e88818-3216-4812-b50e-6fabe0cb659d","order_by":0,"name":"Anwer Sabah Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACNgaGBBAtB+eyEavFGKKUGC0wkNgA00IQ8Ek3PGD4ueNwev/8HgOGD2WHGfjYEwg4TOZAAmPvmcO5M47xGDDOOHeYgY3nAQEtEgkJDLxtt3MbgFqYedsOg0QIa2H823Y7XR6k5S+xWoCG304wAGlhJFbLYdm2/4Ybj6UVHOw5l85D0C/yM3ISH75tS5OXO3x444MfZdZy8u0EbGFg4Ek4AGOCGDzQuMUH2A+gixDUMgpGwSgYBSMMAAAidD4A8hKtwgAAAABJRU5ErkJggg==","orcid":"","institution":"Technical Institute of Al-Najaf, Al-Furat Al-Awsat Technical University","correspondingAuthor":true,"prefix":"","firstName":"Anwer","middleName":"Sabah","lastName":"Ahmed","suffix":""},{"id":578849522,"identity":"4db06f85-372c-4100-842d-2740abfec7ae","order_by":1,"name":"Yousif Jawad Kadhim Nukhailawi","email":"","orcid":"","institution":"Technical Institute of Al-Najaf, Al-Furat Al-Awsat Technical University","correspondingAuthor":false,"prefix":"","firstName":"Yousif","middleName":"Jawad Kadhim","lastName":"Nukhailawi","suffix":""},{"id":578849523,"identity":"82c4aa79-c45c-4dcd-ac48-08a4ddd1abcf","order_by":2,"name":"Heyam A. 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A Genetic Algorithm (GA) is employed to optimize the offset factor within the check-node update, addressing the difficulty of selecting a globally optimal offset for the irregular BG2 structure. The GA operates as a meta-heuristic optimizer that minimizes the bit-error rate (BER) over a wide range of signal-to-noise ratios (SNRs). Simulation results show that the proposed GA-optimized A-OMS decoder achieves performance within 0.2\u0026ndash;0.4 dB of belief-propagation decoding, while providing coding gains of 0.3\u0026ndash;0.6 dB and up to a 4.6\u0026times; BER reduction compared with conventional fixed-offset OMS. These improvements are obtained with approximately 35% of the computational complexity of BP decoding. 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