Machine Learning-based Fingerprinting Localization in 5G Cellular Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151345" target="_blank" >RIV/00216305:26220/24:PU151345 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13164/eeict.2024.222" target="_blank" >10.13164/eeict.2024.222</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning-based Fingerprinting Localization in 5G Cellular Networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings II of the 30th Conference STUDENT EEICT 2024: Selected papers
ISBN
978-80-214-6230-4
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
222-226
Publisher name
Brno University of Technology, Faculty of Electronic Engineering and Communication
Place of publication
Brno
Event location
Brno
Event date
Apr 23, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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