Random forest learning method to identify different objects using channel estimations from VLC link

Mehmet C. Ilter; Alexis A. Dowhuszko; Kiran K. Vangapattu; Kubra Kutlu; Jyri Hämäläinen

doi:10.1109/PIMRC48278.2020.9217131

Random forest learning method to identify different objects using channel estimations from VLC link

Mehmet C. Ilter, Alexis A. Dowhuszko, Kiran K. Vangapattu, Kubra Kutlu, Jyri Hämäläinen

Centre Tecnologic de les Telecomunicacions de Catalunya

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

4 Sitaatiot (Scopus)

170 Lataukset (Pure)

Abstrakti

This paper demonstrates the feasibility of using supervised learning algorithms to identify the presence of different objects, taking advantage of the effect that they create on the VLC channel gains. For this purpose, a software-defined VLC link is implemented using a Phosphor-converted LED, whose light intensity is modulated by an Optical OFDM frame that includes synchronization words and pilot sequences for channel estimation. Actual estimated channel gains are collected in the receiver, which are used to train and assess the performance of the Random Forest classifier. The accuracy of the monitoring system is evaluated using three different objects, showing an accuracy in the order of 90% in detecting the objects, even when they take different positions when obstructing the VLC link.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020
Kustantaja	IEEE
Sivumäärä	6
ISBN (elektroninen)	9781728144900
DOI - pysyväislinkit	https://doi.org/10.1109/PIMRC48278.2020.9217131
Tila	Julkaistu - elok. 2020
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - Virtual, Online Kesto: 31 elok. 2020 → 3 syysk. 2020

Julkaisusarja

Nimi	IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
Kustantaja	IEEE
ISSN (painettu)	2166-9570
ISSN (elektroninen)	2166-9589

Conference

Conference	IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
Lyhennettä	PIMRC
Kaupunki	Virtual, Online
Ajanjakso	31/08/2020 → 03/09/2020
Muu	Virtual conference

Pääsy asiakirjaan

10.1109/PIMRC48278.2020.9217131

ELEC_Ilter_etal_Random-Forest-Learning_PIMRC2020Accepted author manuscript, 1,06 MB

Muut tiedostot ja linkit

Link to publication in Scopus

ATTRACT: breAkThrough innovaTion pRogrAmme for a pan-European Detection and Imaging eCosysTem
Ekman, K. (Vastuullinen tutkija), Blej, A. (Projektin jäsen) & Kar, S. (Projektin jäsen)
01/08/2018 → 30/11/2020
Projekti: EU: Framework programmes funding

Siteeraa tätä

Ilter, M. C., Dowhuszko, A. A., Vangapattu, K. K., Kutlu, K., & Hämäläinen, J. (2020). Random forest learning method to identify different objects using channel estimations from VLC link. teoksessa Proceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020 Artikkeli 9217131 (IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops). IEEE. https://doi.org/10.1109/PIMRC48278.2020.9217131

Ilter, Mehmet C. ; Dowhuszko, Alexis A. ; Vangapattu, Kiran K. et al. / Random forest learning method to identify different objects using channel estimations from VLC link. Proceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020. IEEE, 2020. (IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops).

@inproceedings{99fda2a23112406db696cd3c83d37de1,

title = "Random forest learning method to identify different objects using channel estimations from VLC link",

abstract = "This paper demonstrates the feasibility of using supervised learning algorithms to identify the presence of different objects, taking advantage of the effect that they create on the VLC channel gains. For this purpose, a software-defined VLC link is implemented using a Phosphor-converted LED, whose light intensity is modulated by an Optical OFDM frame that includes synchronization words and pilot sequences for channel estimation. Actual estimated channel gains are collected in the receiver, which are used to train and assess the performance of the Random Forest classifier. The accuracy of the monitoring system is evaluated using three different objects, showing an accuracy in the order of 90% in detecting the objects, even when they take different positions when obstructing the VLC link.",

keywords = "Indoor monitoring, Optical OFDM, Random Forest, Software-defined, Supervised Learning, VLC system",

author = "Ilter, {Mehmet C.} and Dowhuszko, {Alexis A.} and Vangapattu, {Kiran K.} and Kubra Kutlu and Jyri H{\"a}m{\"a}l{\"a}inen",

note = "| openaire: EC/H2020/777222/EU//ATTRACT; IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC ; Conference date: 31-08-2020 Through 03-09-2020",

year = "2020",

month = aug,

doi = "10.1109/PIMRC48278.2020.9217131",

language = "English",

series = "IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops",

publisher = "IEEE",

booktitle = "Proceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020",

address = "United States",

}

Ilter, MC , Dowhuszko, AA, Vangapattu, KK, Kutlu, K & Hämäläinen, J 2020, Random forest learning method to identify different objects using channel estimations from VLC link. julkaisussa Proceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020., 9217131, IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops, IEEE, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Virtual, Online, 31/08/2020. https://doi.org/10.1109/PIMRC48278.2020.9217131

Random forest learning method to identify different objects using channel estimations from VLC link. / Ilter, Mehmet C.; Dowhuszko, Alexis A.; Vangapattu, Kiran K. et al.
Proceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020. IEEE, 2020. 9217131 (IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Random forest learning method to identify different objects using channel estimations from VLC link

AU - Ilter, Mehmet C.

AU - Dowhuszko, Alexis A.

AU - Vangapattu, Kiran K.

AU - Kutlu, Kubra

AU - Hämäläinen, Jyri

N1 - | openaire: EC/H2020/777222/EU//ATTRACT

PY - 2020/8

Y1 - 2020/8

N2 - This paper demonstrates the feasibility of using supervised learning algorithms to identify the presence of different objects, taking advantage of the effect that they create on the VLC channel gains. For this purpose, a software-defined VLC link is implemented using a Phosphor-converted LED, whose light intensity is modulated by an Optical OFDM frame that includes synchronization words and pilot sequences for channel estimation. Actual estimated channel gains are collected in the receiver, which are used to train and assess the performance of the Random Forest classifier. The accuracy of the monitoring system is evaluated using three different objects, showing an accuracy in the order of 90% in detecting the objects, even when they take different positions when obstructing the VLC link.

AB - This paper demonstrates the feasibility of using supervised learning algorithms to identify the presence of different objects, taking advantage of the effect that they create on the VLC channel gains. For this purpose, a software-defined VLC link is implemented using a Phosphor-converted LED, whose light intensity is modulated by an Optical OFDM frame that includes synchronization words and pilot sequences for channel estimation. Actual estimated channel gains are collected in the receiver, which are used to train and assess the performance of the Random Forest classifier. The accuracy of the monitoring system is evaluated using three different objects, showing an accuracy in the order of 90% in detecting the objects, even when they take different positions when obstructing the VLC link.

KW - Indoor monitoring

KW - Optical OFDM

KW - Random Forest

KW - Software-defined

KW - Supervised Learning

KW - VLC system

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U2 - 10.1109/PIMRC48278.2020.9217131

DO - 10.1109/PIMRC48278.2020.9217131

M3 - Conference article in proceedings

AN - SCOPUS:85094141446

T3 - IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops

BT - Proceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020

PB - IEEE

T2 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

Y2 - 31 August 2020 through 3 September 2020

ER -

Ilter MC , Dowhuszko AA, Vangapattu KK, Kutlu K, Hämäläinen J. Random forest learning method to identify different objects using channel estimations from VLC link. julkaisussa Proceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020. IEEE. 2020. 9217131. (IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops). doi: 10.1109/PIMRC48278.2020.9217131

Random forest learning method to identify different objects using channel estimations from VLC link

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

ATTRACT: breAkThrough innovaTion pRogrAmme for a pan-European Detection and Imaging eCosysTem

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