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Inftech."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Zusammenfassung<\/jats:title>\n          <jats:p>Die n\u00e4chste Generation drahtloser Netzwerke sieht sich mit der Herausforderung konfrontiert, strengere Leistungsanforderungen zu erf\u00fcllen, die von ultra-zuverl\u00e4ssigen industriellen Anwendungen bis hin zu hochperformanten Breitbanddiensten reichen. Traditionelle Drive-Tests zur Diagnose und Optimierung der Netzwerkleistung erweisen sich zunehmend als unzureichend, da sie mit hohen Kosten sowie r\u00e4umlichen und zeitlichen Einschr\u00e4nkungen verbunden sind. Der Minimization of Drive Tests (MDT)-Standard adressiert diese Herausforderungen, indem er Daten direkt vom Endger\u00e4t, dem sogenannten User Equipment (UE), unter realen Betriebsbedingungen sammelt und so umfangreiche, qualitativ hochwertige Datens\u00e4tze bereitstellt. Der Fokus dieses Beitrags liegt auf der Untersuchung der Anwendung von MDT-Daten in Kombination mit k\u00fcnstlicher Intelligenz (KI) f\u00fcr Echtzeitanalysen, dynamische Optimierung und vorausschauende Entscheidungsfindung in hochentwickelten Radiozugangsnetzen. Es wird dargelegt, dass die Kombination aus KI-gest\u00fctzten Analysen mit MDT-Messungen eine effektive Modellierung von Nutzerbewegungen, Interferenzmustern und Versorgungsqualit\u00e4t erm\u00f6glicht und somit eine proaktive Ressourcenallokation und schnellere Fehlerbehebung unterst\u00fctzt. Die Integration dieser Methoden in Digital Twin-Frameworks erlaubt zudem die Pr\u00fcfung und Verfeinerung von Netzkonfigurationen in einer virtuellen Umgebung, bevor sie im Live-Betrieb Anwendung finden. Die Ergebnisse betonen die zunehmende Relevanz von KI in Telekommunikationssystemen und positionieren MDT als ein entscheidendes Element f\u00fcr selbstoptimierende Netzwerkarchitekturen der n\u00e4chsten Generation.<\/jats:p>","DOI":"10.1007\/s00502-025-01323-3","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T12:31:52Z","timestamp":1746448312000},"page":"235-244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Auf dem Weg zum KI-gest\u00fctzten Mobilfunknetz: die Rolle von MDT-Daten zur Echtzeitoptimierung","Toward AI-driven wireless networks: the role of the minimization of drive-test standard in real-time spatiotemporal performance monitoring"],"prefix":"10.1007","volume":"142","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5247-715X","authenticated-orcid":false,"given":"Wilfried","family":"Wiedner","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukas","family":"Eller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariam","family":"Mussbah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Svoboda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,5]]},"reference":[{"key":"1323_CR1","unstructured":"3rd Generation Partnership Project (2020) Universal mobile telecommunications system (umts); lte; universal terrestrial radio access (utra) and evolved universal terrestrial radio access (e-utra); radio measurement collection for minimization of drive tests (mdt); overall description. stage 2; (3GPP TS 37.320 version 16.2.0 Release 16)."},{"key":"1323_CR2","unstructured":"3rd Generation Partnership Project (2020). 5g; nr; radio resource control (rrc); protocol specification (3GPP TS 38.331 version 15.3.0 Release 15)."},{"key":"1323_CR3","unstructured":"3rd Generation Partnership Project (2021). 5g; Management and orchestration; 5g performance measusurements; (3GPP TS 28.552 version 16.9.0 Release 16)."},{"key":"1323_CR4","doi-asserted-by":"crossref","unstructured":"Micheli D, Diamanti R (2019) Statistical analysis of interference in a\u00a0reallte access network by massive collection of mdt radio measurement data fromsmartphones. 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