Binary signaling design for visible light communication: a deep learning framework

Hoon Lee, Inkyu Lee, Tony Q.S. Quek, Sang Hyun Lee

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


This paper develops a deep learning framework for the design of on-o keying (OOK) based binary signaling transceiver in dimmable visible light communication (VLC) systems. The dimming support for the OOK optical signal is achieved by adjusting the number of ones in a binary codeword, which boils down to a combinatorial design problem for the codebook of a constant weight code (CWC) over signal-dependent noise channels. To tackle this challenge, we employ an autoencoder (AE) approach to learn a neural network of the encoder-decoder pair that reconstructs the output identical to an input. In addition, optical channel layers and binarization techniques are introduced to reflect the physical and discrete nature of the OOK-based VLC systems. The VLC transceiver is designed and optimized via the end-to-end training procedure for the AE. Numerical results verify that the proposed transceiver performs better than baseline CWC schemes.

Original languageEnglish
Pages (from-to)18131-18142
Number of pages12
JournalOptics Express
Issue number14
Publication statusPublished - 2018 Jul 9

Bibliographical note

Funding Information:
National Research Foundation of Korea (NRF) (2015R1C1A1A01052529, 2017R1A2B3012316); Korea University Grant.

Publisher Copyright:
© 2018 Optical Society of America.

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


Dive into the research topics of 'Binary signaling design for visible light communication: a deep learning framework'. Together they form a unique fingerprint.

Cite this