Enhancing Path Loss Predictions for 2.6GHz 4G LTE in Urban Areas: A Case Study of Ibadan

Enhancing Path Loss Predictions for 2.6GHz 4G LTE in Urban Areas: A Case Study of Ibadan | 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 Enhancing Path Loss Predictions for 2.6GHz 4G LTE in Urban Areas: A Case Study of Ibadan Oluyemi E. Adetoyi, Olamide O. Babalola This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6247587/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 Accurate path loss estimation is crucial for the design and performance optimization of mobile communication networks. However, existing empirical models often fail to deliver reliable predictions across diverse environments. This study focuses on developing an optimized path loss prediction model for 2.6 GHz 4G Long Term Evolution (LTE) networks in urban Ibadan, Nigeria. Three widely used empirical models COST-231 Hata, Stanford University Interim (SUI), and ECC-33 were evaluated by comparing their predicted values with real-world measurement data. Performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Standard Deviation (SD), identified the ECC-33 model as the most accurate base model, yielding an RMSE of 6.68dB, MAE of 4.89dB, and SD of 5.80dB. An improved ECC-33 model was developed by training a linear regression with 70% of the collected field data to obtain optimised ECC-33 parameters. When validated with the remaining 30%, it demonstrates an enhanced accuracy, achieving an RMSE of 2.88dB, MAE of 2.46dB, and SD of 3.20dB. This optimized model can be a reliable tool for planning and optimizing 4G LTE networks in urban Ibadan, thus improving voice and data service quality. Path Loss 4G LTE COST 231 Hata SUI ECC-33 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-6247587","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444444179,"identity":"48da8316-df13-473a-b979-edc68ca1ae63","order_by":0,"name":"Oluyemi E. Adetoyi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYJCCAwwMzCCa8QGQ4OEjRQuzAUgLG5EWgbWwSYBJQmoNjncnHmCosZZjYG8+Vvk1x06GjYH54aMb+LScObvhAMOxdGMGnmNpt2W3JQMdxmZsnINPy41coBa2w4kNEjlmtyW3MQO18LBJ49Vy/y1Qy7/D9Q0S+d+KJbfVE6HlBu+GA4xthxMYJHLYGD9uO0xYi+QZoMMS+9IN23iOGUszbjvOw8ZMwC98x89u/vDhm7U8P3vzw48/t1XbgxiP8WlROAAkEhgg0cHMAxJixqMcBOQbkDiMPwioHgWjYBSMgpEJAMeFR0vyGe75AAAAAElFTkSuQmCC","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Oluyemi","middleName":"E.","lastName":"Adetoyi","suffix":""},{"id":444444182,"identity":"13bf0f0b-f561-4f55-b321-aa00727ca5d2","order_by":1,"name":"Olamide O. Babalola","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Olamide","middleName":"O.","lastName":"Babalola","suffix":""}],"badges":[],"createdAt":"2025-03-17 20:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6247587/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6247587/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85463902,"identity":"57e55948-8a35-4a36-9fbd-b7a9ed7bcad7","added_by":"auto","created_at":"2025-06-26 08:02:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":555370,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchPaperonPathLoss.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6247587/v1_covered_29398b5f-58c5-44c8-9f09-2fd997df9027.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Path Loss Predictions for 2.6GHz 4G LTE in Urban Areas: A Case Study of Ibadan","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Path Loss, 4G LTE, COST 231 Hata, SUI, ECC-33","lastPublishedDoi":"10.21203/rs.3.rs-6247587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6247587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate path loss estimation is crucial for the design and performance optimization of mobile communication networks. However, existing empirical models often fail to deliver reliable predictions across diverse environments. This study focuses on developing an optimized path loss prediction model for 2.6 GHz 4G Long Term Evolution (LTE) networks in urban Ibadan, Nigeria. Three widely used empirical models COST-231 Hata, Stanford University Interim (SUI), and ECC-33 were evaluated by comparing their predicted values with real-world measurement data. Performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Standard Deviation (SD), identified the ECC-33 model as the most accurate base model, yielding an RMSE of 6.68dB, MAE of 4.89dB, and SD of 5.80dB. An improved ECC-33 model was developed by training a linear regression with 70% of the collected field data to obtain optimised ECC-33 parameters. When validated with the remaining 30%, it demonstrates an enhanced accuracy, achieving an RMSE of 2.88dB, MAE of 2.46dB, and SD of 3.20dB. This optimized model can be a reliable tool for planning and optimizing 4G LTE networks in urban Ibadan, thus improving voice and data service quality.\u003c/p\u003e","manuscriptTitle":"Enhancing Path Loss Predictions for 2.6GHz 4G LTE in Urban Areas: A Case Study of Ibadan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 10:29:16","doi":"10.21203/rs.3.rs-6247587/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"55273d6f-4080-4971-b6b5-f43bfac7194b","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-26T07:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 10:29:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6247587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6247587","identity":"rs-6247587","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}