Publication detail

Machine Learning-based Fingerprinting Localization in 5G Cellular Networks

LE, T. D. MAŠEK, P.

Original Title

Machine Learning-based Fingerprinting Localization in 5G Cellular Networks

Type

conference paper

Language

English

Original 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

Fingerprinting Localization; Machine Learning; 5G New Radio; NB-IoT; LTE-M; Random Forest; XGBoost; Support Vector Machine

Authors

LE, T. D.; MAŠEK, P.

Released

23. 4. 2024

Publisher

Brno University of Technology, Faculty of Electronic Engineering and Communication

Location

Brno

ISBN

978-80-214-6230-4

Book

Proceedings II of the 30th Conference STUDENT EEICT 2024: Selected papers

Edition

1

Pages from

222

Pages to

226

Pages count

5

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf

BibTex

@inproceedings{BUT188618,
  author="Dinh Thao {Le} and Pavel {Mašek}",
  title="Machine Learning-based Fingerprinting Localization in 5G Cellular Networks",
  booktitle="Proceedings II of the 30th Conference STUDENT EEICT 2024: Selected papers",
  year="2024",
  series="1",
  pages="222--226",
  publisher="Brno University of Technology, Faculty of Electronic Engineering and Communication",
  address="Brno",
  doi="10.13164/eeict.2024.222",
  isbn="978-80-214-6230-4",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf"
}