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

By Hoon Lee, Inkyu Lee, Tony Q. S. Quek, Sang Hyun Lee. 2018

40 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering

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In Electrical and Electronic Engineering

1. Tony Q. S. Quek
2. Sang Hyun Lee
3. Hoon Lee
4. Inkyu Lee

Citations in the corpus

• Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, neutral): “[10], [11], [16].”
• Constrained Deep Learning for Wireless Resource Management (2019, methodology): “Recently, a penalizing strategy has been introduced [5], [6], [15] to transform the original constrained problem to an unconstrained one by augmenting a regularization term as”
• Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications (2018, methodology): “For implementing the OOK modulation, the parameterized sigmoid activation is adopted at the end of the encoding network with the aid of the annealing-based multi-stage training strategy [9].”