Traffic Prediction and Network Load Forecasting in Mobile Networks Using Machine Learning

Traffic Prediction and Network Load Forecasting in Mobile Networks Using Machine 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 Traffic Prediction and Network Load Forecasting in Mobile Networks Using Machine Learning SURESH KUMAR K This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5480268/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 Precisely, the accuracy in predicting network traffic with precise forecasting of load at the right time are very important because they contribute directly towards the optimal distribution of resources and prevention of future congestion in mobile networks. This work is based on predicting future network traffic patterns and load through the implementation of machine learning methods applied based on supervised algorithms, like Random Forests and Support Vector Machines (SVM). The proposed models use historical network data, environmental factors, and user behaviour to make real-time predictions about the traffic demand under which network resources can proactively be adjusted in advance. The experiments show that the machine learning-based models have better performance than the traditional ones, which means a higher accuracy in traffic demand prediction and better effectiveness in network management. This study shows how powerful machine learning is in making mobile networks offer better performance and reliability through effective ensuring optimal resource distribution and resisting congestion. Network Traffic Prediction Congestion Random Forests Support Vector Machine Network Management Performance Machine Learning 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. 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-5480268","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380406655,"identity":"b96a03fe-0771-453a-abfe-4da9b6aaff6b","order_by":0,"name":"SURESH KUMAR K","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBADZn4QmVBAihbJBpAWA1KsMTgAJolQKT8j/eFn3j127MbnVyd+eGDAIM8vdoCA4TdyjKV5niUzm914u1kC6DDDmbMTCGiRyGGQ5jnADNRydgNIS4LBbQJagA57/JvnQD2z8Yyzm38QpYXhRoIZ0JbDzAb8vduIs8XgzBszyzkHjjNL3ODdZpFgIEHYL/Lt6Y9vvDlQnczff3bzzR8VNvL80oQcBgRMPAwMyQwSYJUShJWDAOMPBgY7Bv4DxKkeBaNgFIyCkQcA8/BChxfQAfwAAAAASUVORK5CYII=","orcid":"","institution":"Chennai Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"SURESH","middleName":"KUMAR","lastName":"K","suffix":""}],"badges":[],"createdAt":"2024-11-19 05:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5480268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5480268/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71422598,"identity":"ff21e034-a245-4cd4-be2b-9baeb7c40e92","added_by":"auto","created_at":"2024-12-14 20:31:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":492848,"visible":true,"origin":"","legend":"","description":"","filename":"TrafficPredictionandNetworkLoadForecastinginMobileNetworksUsingMachineLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5480268/v1_covered_9ef2093d-d97a-4a78-9dfc-b9c683866381.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Traffic Prediction and Network Load Forecasting in Mobile Networks Using Machine Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Network Traffic Prediction, Congestion, Random Forests, Support Vector Machine, Network Management, Performance, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-5480268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5480268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecisely, the accuracy in predicting network traffic with precise forecasting of load at the right time are very important because they contribute directly towards the optimal distribution of resources and prevention of future congestion in mobile networks. 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