The Vienna 4G/5G Drive-Test Dataset

The Vienna 4G/5G Drive-Test Dataset | 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 data-descriptor The Vienna 4G/5G Drive-Test Dataset Wilfried Wiedner, Lukas Eller, Mariam Mussbah, Dominik Rössler, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8927168/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 Machine learning for mobile network analysis, planning, and optimization is often limited by the lack of large, comprehensive real-world datasets. This paper introduces the Vienna 4G/5G Drive-Test Dataset, a city-scale open dataset of georeferenced Long Term Evolution (LTE) and 5G New Radio (NR) measurements collected across Vienna, Austria. The dataset combines passive wideband scanner observations with active handset logs, providing complementary network-side and user-side views of deployed radio access networks. The measurements cover diverse urban and suburban settings and are aligned with time and location information to support consistent evaluation. For a representative subset of base stations (BSs), we provide inferred deployment descriptors, including estimated BS locations, sector azimuths, and antenna heights. The release further includes high-resolution building and terrain models, enabling geometry-conditioned learning and calibration of deterministic approaches such as ray tracing. To facilitate practical reuse, the data are organized into scanner, handset, estimated cell information, and city-model components, and the accompanying documentation describes the available fields and intended joins between them. The dataset enables reproducible benchmarking across environment-aware learning, propagation modeling, coverage analysis, and ray-tracing calibration workflows. 4G 5G LTE NR drive test dataset digital twin ray tracing propagation modeling 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-8927168","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":594945564,"identity":"ba8cc6c9-f13b-4062-9992-77cf9ae025d8","order_by":0,"name":"Wilfried Wiedner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABI0lEQVRIie3RMUvDQBTA8XcEMiVkvVLafoUXAkGh1K+SEOiqmx0c7ghcFqGrSz+FII6RB8mSD5BBsF3cCgGXgmLNKUq1qeDmcH9uenc/3nAAJtN/zLJEAwhM6OO0A09Po18Jk1ffSE/tkrzLaAL6fZsmWOzedhAsmUxnZ+OB4Ilcrm/Hp8HDZciXs3vwspw9NR2EmJQVTgPB49RfVNPjm8IJeVQ9Aq8ii3dsCYllK4EUi36s+q4iDFtSx4oAaoADREqBW02yZ1dtMVCavBKMarA2h0n+vsVyVY5oayIIsAa7a8vJB0kCNVqlvYVKkBfT801UkONXsTqq9klvTndSvEwGc6dcNWs1QS+la39zQcNhSVTP9slX9s+B8/lZJpPJZPp7b2Gnb7cAiD9QAAAAAElFTkSuQmCC","orcid":"","institution":"TU Wien","correspondingAuthor":true,"prefix":"","firstName":"Wilfried","middleName":"","lastName":"Wiedner","suffix":""},{"id":594945565,"identity":"c70c4b0f-9ca7-4878-9cbd-96445525cbb5","order_by":1,"name":"Lukas Eller","email":"","orcid":"","institution":"TU Wien","correspondingAuthor":false,"prefix":"","firstName":"Lukas","middleName":"","lastName":"Eller","suffix":""},{"id":594945566,"identity":"4cdfc2d9-c026-4736-bb2b-9a80ed4ddb76","order_by":2,"name":"Mariam Mussbah","email":"","orcid":"","institution":"TU Wien","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"","lastName":"Mussbah","suffix":""},{"id":594945567,"identity":"b68293cd-7030-4ff9-9a68-80ad650fb8d9","order_by":3,"name":"Dominik Rössler","email":"","orcid":"","institution":"TU Wien","correspondingAuthor":false,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Rössler","suffix":""},{"id":594945568,"identity":"50a0c329-e047-403f-9957-82f048666fc6","order_by":4,"name":"Valerian Maresch","email":"","orcid":"","institution":"TU Wien","correspondingAuthor":false,"prefix":"","firstName":"Valerian","middleName":"","lastName":"Maresch","suffix":""},{"id":594945569,"identity":"cc54a79c-1c0d-4889-b715-8e7e641f0f16","order_by":5,"name":"Philipp Svoboda","email":"","orcid":"","institution":"TU Wien","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Svoboda","suffix":""},{"id":594945570,"identity":"64b782b7-afa7-4324-99a7-e8a84ca07978","order_by":6,"name":"Markus Rupp","email":"","orcid":"","institution":"TU Wien","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Rupp","suffix":""}],"badges":[],"createdAt":"2026-02-20 15:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8927168/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8927168/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103507150,"identity":"6718ccb2-f42d-4af9-a133-5d89eacd2765","added_by":"auto","created_at":"2026-02-26 13:40:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3413661,"visible":true,"origin":"","legend":"","description":"","filename":"ViennaDatasetSpringerNature.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8927168/v1_covered_4aa72473-f277-4696-8fbf-f37456154799.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Vienna 4G/5G Drive-Test Dataset","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":true,"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":"4G, 5G, LTE, NR, drive test, dataset, digital twin, ray tracing, propagation modeling","lastPublishedDoi":"10.21203/rs.3.rs-8927168/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8927168/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Machine learning for mobile network analysis, planning, and optimization is often limited by the lack of large, comprehensive real-world datasets. This paper introduces the Vienna 4G/5G Drive-Test Dataset, a city-scale open dataset of georeferenced Long Term Evolution (LTE) and 5G New Radio (NR) measurements collected across Vienna, Austria. The dataset combines passive wideband scanner observations with active handset logs, providing complementary network-side and user-side views of deployed radio access networks. The measurements cover diverse urban and suburban settings and are aligned with time and location information to support consistent evaluation. For a representative subset of base stations (BSs), we provide inferred deployment descriptors, including estimated BS locations, sector azimuths, and antenna heights. The release further includes high-resolution building and terrain models, enabling geometry-conditioned learning and calibration of deterministic approaches such as ray tracing. To facilitate practical reuse, the data are organized into scanner, handset, estimated cell information, and city-model components, and the accompanying documentation describes the available fields and intended joins between them. The dataset enables reproducible benchmarking across environment-aware learning, propagation modeling, coverage analysis, and ray-tracing calibration workflows.","manuscriptTitle":"The Vienna 4G/5G Drive-Test Dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 03:20:55","doi":"10.21203/rs.3.rs-8927168/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":"2f0fae53-987d-43d7-b4a6-d06e1c0decc9","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-08T11:39:07+00:00","index":34,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T12:40:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 03:20:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8927168","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8927168","identity":"rs-8927168","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}