Original title: Machine Learning-based Fingerprinting Localization in 5G Cellular Networks
Authors: Dinh Le, Thao ; Mašek, Pavel
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: This study explores the viability of employing machine learning (ML)-based fingerprinting localization in 5G heterogeneous cellular networks. We conducted an extensive measurement campaign to collect data and utilized them to train three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). The findings reveal that RF delivers the highest accuracy among the three ML algorithms. Furthermore, the results indicate that 5G New Radio (NR) can benefit the most from this localization method due to the dense deployment of base stations, achieving median localization errors of 17.5 m and 106 m during the validation and testing phases, respectively.
Keywords: 5G New Radio; Fingerprinting Localization; LTE-M; Machine Learning; NB-IoT; Random Forest; Support Vector Machine; XGBoost
Host item entry: Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers, ISBN 978-80-214-6230-4, ISSN 2788-1334

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/249320

Permalink: http://www.nusl.cz/ntk/nusl-622532


The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers