Abstract

To enhance commercial implementation, visible light communication (VLC) should facilitate dimming control and reliable data transmission simultaneously, where users can adjust the brightness levels arbitrarily. Constant weight code (CWC) based dimming method can achieve higher spectral efficiency, but the codewords need further selection to exponentially map the massage. In this paper, based on CWC, we proposed a method to achieve dimming control as well as improve error performance via optimizing the average Hamming distance of the codeword set. Simulation results show that the optimal codeword set can achieve better error performance.

Highlights

  • White light emitting diode (LED) based lighting has been emerging as a promising technology to overtake the traditional lighting [1, 2]

  • Visible light communication (VLC) system utilizing the superior modulation of LED can achieve data transmission and illumination simultaneously [3, 4]

  • According to the proposed principles, the performance can be divided into four types: “BEST”, “GOOD”, “BAD” and “WORST”

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Summary

Introduction

White light emitting diode (LED) based lighting has been emerging as a promising technology to overtake the traditional lighting (e.g. incandescent and fluorescent lighting) [1, 2]. Visible light communication (VLC) system utilizing the superior modulation of LED can achieve data transmission and illumination simultaneously [3, 4]. The general modulation or coding methods achieve dimming control via changing the ratio of “1” and “0”. Based on binary entropy function, Kwon proposed inverse source coding (ISC) scheme in [7] to meet the theoretical maximum spectral efficiency under a given dimming target. Constant weight code (CWC) based dimming method proposed in [8] can achieve higher spectral efficiency, but the codewords need further selection to exponentially map the massage. Inspired by [8], in this paper, we utilize CWC to achieve dimming control as well as improve error performance via optimizing the average Hamming distance of the codeword set.

Principles for optimization
Conclusions
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