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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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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.

Original languageEnglish
Title of host publicationProceedings of the IEEE 31th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728144900
DOIs
Publication statusPublished - Aug 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Symposium on Personal, Indoor, and Mobile Radio Communications - Virtual, Online
Duration: 31 Aug 20203 Sep 2020

Publication series

NameIEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
PublisherIEEE
ISSN (Print)2166-9570
ISSN (Electronic)2166-9589

Conference

ConferenceIEEE International Symposium on Personal, Indoor, and Mobile Radio Communications
Abbreviated titlePIMRC
CityVirtual, Online
Period31/08/202003/09/2020
OtherVirtual conference

Keywords

  • Indoor monitoring
  • Optical OFDM
  • Random Forest
  • Software-defined
  • Supervised Learning
  • VLC system

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