Open AccessDeep learning based transceiver design for multi-colored VLC systems
Author(s) -
Hoon Lee,
Inkyu Lee,
Sang Hyun Lee
Publication year - 2018
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.006222
Subject(s) - visible light communication , computer science , transmitter , rgb color model , transceiver , modulation (music) , autoencoder , channel (broadcasting) , colored , artificial intelligence , optics , electronic engineering , deep learning , light emitting diode , telecommunications , wireless , physics , engineering , materials science , acoustics , composite material
This paper presents a deep-learning (DL) based approach to the design of multi-colored visible light communication (VLC) systems where RGB light-emitting diode (LED) lamps accomplish multi-dimensional color modulation under color and illuminance requirements. It is aimed to identify a pair of multi-color modulation transmitter and receiver leading to efficient symbol recovery performance. To this end, an autoencoder (AE), an unsupervised deep learning technique, is adopted to train the end-to-end symbol recovery process that includes the VLC transceiver pair and a channel layer characterizing the optical channel along with additional LED intensity control features. As a result, the VLC transmitter and receiver are jointly designed and optimized. Intensive numerical results demonstrate that the learned VLC system outperforms existing techniques in terms of the average symbol error probability. This framework sheds light on the viability of DL techniques in the optical communication system design.