Positioning in WLAN Networks Based on SVM Algorithms and Space Segmentation

Due to the widespread availability of WLAN networks, positioning techniques in these environments have become a subject of intense research. In this paper, a combination of Support Vector Classification (SVC) and Support Vector Regression (SVR) machine learning algorithms was used, along with space...

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Veröffentlicht in:Telecommunications Forum S. 1 - 4
Hauptverfasser: Burdzic, Marko, Neskovic, Aleksandar, Neskovic, Natasa
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 26.11.2024
Schlagworte:
Location awareness
Machine learning algorithms
Static VAr compensators
Support vector machines
Telecommunications
Vectors
Wireless LAN
ISSN:2994-5828
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Zusammenfassung:Due to the widespread availability of WLAN networks, positioning techniques in these environments have become a subject of intense research. In this paper, a combination of Support Vector Classification (SVC) and Support Vector Regression (SVR) machine learning algorithms was used, along with space segmentation, to solve the problem of user localization in a WLAN network. The proposed technique was thoroughly tested in a real WLAN environment. The lowest mean positioning error was achieved when the space was divided into 6 subspaces, amounting to 7.69m.
ISSN:2994-5828
DOI:10.1109/TELFOR63250.2024.10819125