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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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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Abstract 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.
AbstractList 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.
Author Burdzic, Marko
Neskovic, Aleksandar
Neskovic, Natasa
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  givenname: Aleksandar
  surname: Neskovic
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  givenname: Natasa
  surname: Neskovic
  fullname: Neskovic, Natasa
  email: natasha@etf.rs
  organization: Univerzitet u Beogradu,Elektrotehnički Fakultet,Beograd,Srbija
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Snippet Due to the widespread availability of WLAN networks, positioning techniques in these environments have become a subject of intense research. In this paper, a...
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SubjectTerms Location awareness
Machine learning algorithms
Static VAr compensators
Support vector machines
Telecommunications
Vectors
Wireless LAN
Title Positioning in WLAN Networks Based on SVM Algorithms and Space Segmentation
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