Average Localization Error Prediction for 5G Networks: An Investigation of Different Machine Learning Algorithms

Simple item page
dc.contributor.authorAltay O.
dc.contributor.authorErel-Özçevik M.
dc.contributor.authorVarol Altay E.
dc.contributor.authorÖzçevik Y.
dc.date.accessioned2024-07-22T08:01:34Z
dc.date.available2024-07-22T08:01:34Z
dc.date.issued2024
dc.description.abstractIn the realm of today’s networking technologies, user localization has been a formidable challenge for recent applications. There are different approaches in pursuit of heightened position detection of an end-user with the help of GPS, Wi-Fi fingerprint and 5G equipment. However, these approaches require both deployment and maintenance costs because of equipment establishment for position tracking. Moreover, they are not capable of minimizing the localization error, especially for indoor scenarios to track the indoor position of an end-user. Hence, there is an urgent need to delve deeper into innovative approaches to drive further advancements in user localization. In response, Machine Learning (ML) approaches have recently been widely adapted to predict the localization of end-users with minimum error. More specifically, average localization error (ALE) of an end-user can be predicted in a cost-effective way by using proper data and ML methods. For this purpose, we have investigated different ML approaches to get an accurate ALE prediction scheme for 5G networks with mobile end-users. Accordingly, an existing dataset is utilized to generate localization data of end-users in which the ALE is directly calculated by Received Signal Strength Indicator. Moreover, three different normalization approaches are applied for the overarching goal of increased data quality. Consequently, six different ML algorithms, including Linear regression, support vector machine with three different kernels, Gaussian process, and ensemble least-squares boosting (LSBoost) are evaluated with respect to a set of evaluation criteria including R, R2, RMSE, and MAE. The evaluation outcomes emphasize that ensemble LSBoost method, in the context of localization prediction, outperforms the other approaches and is sufficient to yield a viable learning strategy for ALE prediction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
dc.identifier.DOI-ID10.1007/s11277-024-11257-2
dc.identifier.issn09296212
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11496
dc.language.isoEnglish
dc.publisherSpringer
dc.subjectAdaptive boosting
dc.subjectCost effectiveness
dc.subjectDigital storage
dc.subjectErrors
dc.subjectForecasting
dc.subjectIndoor positioning systems
dc.subjectLearning systems
dc.subjectQueueing networks
dc.subjectSupport vector machines
dc.subject5g
dc.subjectAverage localization error
dc.subjectEnd-users
dc.subjectEnsemble least-square boosting
dc.subjectError prediction
dc.subjectLeast Square
dc.subjectLocalisation
dc.subjectLocalization errors
dc.subjectMachine learning algorithms
dc.subjectMachine-learning
dc.subject5G mobile communication systems
dc.titleAverage Localization Error Prediction for 5G Networks: An Investigation of Different Machine Learning Algorithms
dc.typeArticle

Files

Collections

Scopus Koleksiyonu