Machine Learning-Assisted Mobile Network Speed Prediction: A Real-World Case Application

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dc.authorscopusidEmrecan Özkan / 59254003100
dc.authorwosidEmrecan Özkan / LVY-9310-2024
dc.contributor.authorÖzkan, Emrecan
dc.contributor.authorYazıcı, İbrahim
dc.contributor.authorEngin, Semih
dc.date.accessioned2025-04-18T10:40:16Z
dc.date.available2025-04-18T10:40:16Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractIn recent times, as the use of mobile devices has significantly increased, there is a growing expectation for uninterrupted and high-quality wireless mobile network services in all areas. In line with this expectation, ongoing performance measurements and analyses are conducted. However, conducting such studies in an area where no measurements have been taken would result in a loss of both cost, increased workload, and time. To prevent such losses, machine learning was developed to predict the speed test results based on data collected previously with traditional measurement devices. This machine learning model has been designed to anticipate the speed test outcomes in an area where no measurements have yet been taken, relying on the similarity to previously measured parameters. In this study, a variety of data from different sources has been used. Various parameters such as different cities, residential areas, mobile device brands, and various generations of mobile networks were utilized as inputs in the machine learning modeling. The performance metric of the mobile network speed test is used as output. Various models were applied to this dataset. The achieved results were compared, leading to the selection of the algorithm that provided the most suitable results for this study. The results overall suggest that the Random Forest Regression model generally performed better in predicting mobile network performance in these cities, while AdaBoost and XGBoost had varying degrees of accuracy and predictive power in different regions. The study can be used in service quality prediction for internet providers by integrating relevant machine learning models into real systems with application programming interfaces (API). © 2024 IEEE.
dc.description.sponsorshipBerdan Civata, B.C.et al.Figes, Koluman, Loodos, Tarsus University.
dc.identifier.citationÖzkan, E., Yazıcı, İ., & Engin, S. (2024, May). Machine Learning-Assisted Mobile Network Speed Prediction: A Real-World Case Application. In 2024 32nd Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
dc.identifier.doi10.1109/SIU61531.2024.10601069
dc.identifier.isbn979-835038896-1
dc.identifier.scopus2-s2.0-85200857309
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7163
dc.indekslendigikaynakScopus
dc.institutionauthorÖzkan, Emrecan
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMachine Learning
dc.subjectMobile Network
dc.subjectPerformance Evaluation
dc.subjectWireless Network
dc.titleMachine Learning-Assisted Mobile Network Speed Prediction: A Real-World Case Application
dc.title.alternativeMakine Öğrenmesi Yardımıyla Mobil Ağ Hız Tahminlemesi: Gerçek Bir Uygulama
dc.typeOther

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