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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Bibliographic Details
Published in:	Telecommunications Forum pp. 1 - 4
Main Authors:	Burdzic, Marko, Neskovic, Aleksandar, Neskovic, Natasa
Format:	Conference Proceeding
Language:	English
Published:	IEEE 26.11.2024
Subjects:	Location awareness Machine learning algorithms Static VAr compensators Support vector machines Telecommunications Vectors Wireless LAN
ISSN:	2994-5828
Online Access:	Get full text
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Summary:	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