Random forest learning method to identify different objects using channel estimations from VLC system
| Main Authors: | Ilter, Mehmet C., Dowhuszko, Alexis A., Vangapattu, Kiran K., Kutlu, Kubra, Hämäläinen, Jyri |
|---|---|
| Format: | Proceeding Journal |
| Bahasa: | eng |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4429756 |
Daftar Isi:
- 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.
- Grant numbers : TERESA - Hybrid TERrEstrial/Satellite Air Interface for 5G and Beyond (TEC2017-90093-C3-1-R).© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.