Machine-Learning-Aided Method for Optimizing Beam Selection and Update Period in 5G Networks and Beyond

Machine-Learning-Aided Method for Optimizing Beam Selection and Update Period in 5G Networks and Beyond | 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 Article Machine-Learning-Aided Method for Optimizing Beam Selection and Update Period in 5G Networks and Beyond Ludwing Marenco, Luiz E. Hupalo, Naylson F. Andrade, Felipe A. P. Figueiredo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4546043/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Finding the optimal beam pair and update time in 5G systems operating at mmWave frequencies is time-intensive and resource-demanding. This intricate procedure calls for the proposal of more intelligent approaches. Therefore, this work proposes a machine learning-based method for optimizing beam pair selection and its update time. The method is structured around three main modules: spatial characterization of beam pair service areas, training of a machine learning model using collected beam pair data, and an algorithm that uses the decision function of the trained model to compute the optimal update time for beam pairs based on the spatial position and velocity of user equipment. When the machine learning model is deployed in the network comprising one single gNB and one single user equipment in an mmWave scenario, improvement in SINR and throughput up to 4% are observed. Improvements are gathered because of a reduction of 87.5% in beam pair selections because of an increase of approximately 2330% in the effective time between successive beam pair searches. This method could offer real-time optimization of the beam pair procedures in 5G networks and beyond. Physical sciences/Engineering Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Information technology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Jul, 2024 Reviews received at journal 02 Jul, 2024 Reviews received at journal 02 Jul, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers invited by journal 21 Jun, 2024 Editor assigned by journal 21 Jun, 2024 Editor invited by journal 12 Jun, 2024 Submission checks completed at journal 11 Jun, 2024 First submitted to journal 07 Jun, 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. 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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-4546043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":318589437,"identity":"51d63bdc-2c50-4340-9c40-2cf3200bfefc","order_by":0,"name":"Ludwing Marenco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACPgYGA4YEBgY5NjC3AMQmANigWoyBDMYGCJsYLUCQ2EC8FonkbRIPd9Sm90k3P3/wwYAhz7yBoJa0MonEM8dz22SOGTbOMGAoljlAUEuO2Y3EtmO5bRIJhs08BgyJMwg7DKIlnU0i/WPzHxK01CQAGYbNDERp4XlW/iOx7YBhm0RO4cweA4liCUJa+NmTNxv+bKuTl5+RvuHDjwqbPIJaoOAwjEGsBgaGOqJVjoJRMApGwQgEAOXTO9wGDd3mAAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Ludwing","middleName":"","lastName":"Marenco","suffix":""},{"id":318589438,"identity":"5a90fccf-b49c-445b-84e9-c076b3f5f565","order_by":1,"name":"Luiz E. 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