A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver
- Authors
- Lee, Hoon; Quek, Tony Q. S.; Lee, Sang Hyun
- Issue Date
- 2월-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Training; Transceivers; Optical transmitters; Optical pulses; Light emitting diodes; Receivers; Neural networks; Visible light communication; deep learning; dimming support; primal-dual method
- Citation
- IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, v.19, no.2, pp.956 - 969
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
- Volume
- 19
- Number
- 2
- Start Page
- 956
- End Page
- 969
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/57890
- DOI
- 10.1109/TWC.2019.2950026
- ISSN
- 1536-1276
- Abstract
- This paper studies a deep learning (DL) framework for the design of binary modulated visible light communication (VLC) transceiver with universal dimming support. The dimming control for the optical binary signal boils down to a combinatorial codebook design so that the average Hamming weight of binary codewords matches with arbitrary dimming target. An unsupervised DL technique is employed for obtaining a neural network to replace the encoder-decoder pair that recovers the message from the optically transmitted signal. In such a task, a novel stochastic binarization method is developed to generate the set of binary codewords from continuous-valued neural network outputs. For universal support of arbitrary dimming target, the DL-based VLC transceiver is trained with multiple dimming constraints, which turns out to be a constrained training optimization that is very challenging to handle with existing DL methods. We develop a new training algorithm that addresses the dimming constraints through a dual formulation of the optimization. Based on the developed algorithm, the resulting VLC transceiver can be optimized via the end-to-end training procedure. Numerical results verify that the proposed codebook outperforms theoretically best constant weight codebooks under various VLC setups.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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