Open AccessBinary signaling design for visible light communication: a deep learning framework
Author(s) -
Hoon Lee,
Inkyu Lee,
Tony Q. S. Quek,
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.26.018131
Subject(s) - visible light communication , transceiver , keying , autoencoder , computer science , codebook , binary number , on off keying , amplitude shift keying , code word , electronic engineering , signal (programming language) , phase shift keying , encoder , optical communication , artificial neural network , decoding methods , algorithm , telecommunications , artificial intelligence , optics , bit error rate , physics , wireless , mathematics , light emitting diode , engineering , programming language , arithmetic , operating system
This paper develops a deep learning framework for the design of on-off 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.