Detail publikace

Machine Learning-based Fingerprinting Localization in 5G Cellular Networks

LE, T. D. MAŠEK, P.

Originální název

Machine Learning-based Fingerprinting Localization in 5G Cellular Networks

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

LE, T. D.; MAŠEK, P.

Vydáno

23. 4. 2024

Nakladatel

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Místo

Brno

ISBN

978-80-214-6230-4

Kniha

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

Strany od

222

Strany do

226

Strany počet

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",
  pages="222--226",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  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"
}