Deep learning based transceiver design for multi-colored VLC systems

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47 Citations (Scopus)


This paper presents a deep-learning (DL) based approach to the design of multicolored 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 e cient 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.

Original languageEnglish
Pages (from-to)6222-6238
Number of pages17
JournalOptics Express
Issue number5
Publication statusPublished - 2018 Mar 5

Bibliographical note

Publisher Copyright:
© 2018 Optical Society of America.

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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