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Title: LED nonlinearity estimation and compensation in VLC systems using probabilistic bayesian learning
Authors: Chen, Chen
Deng, Xiong
Yang, Yanbing
Du, Pengfei
Yang, Helin
Zhao, Lifan
Keywords: Nonlinearity Estimation and Compensation
Engineering::Electrical and electronic engineering
Light Emitting Diode
Issue Date: 2019
Source: Chen, C., Deng, X., Yang, Y., Du, P., Yang, H., & Zhao, L. (2019). LED Nonlinearity Estimation and Compensation in VLC Systems Using Probabilistic Bayesian Learning. Applied Sciences, 9(13), 2711-. doi:10.3390/app9132711
Series/Report no.: Applied Sciences
Abstract: In this paper, we propose and evaluate a novel light-emitting diode (LED) nonlinearity estimation and compensation scheme using probabilistic Bayesian learning (PBL) for spectral-efficient visible light communication (VLC) systems. The nonlinear power-current curve of the LED transmitter can be accurately estimated by exploiting PBL regression and hence the adverse effect of LED nonlinearity can be efficiently compensated. Simulation results show that, in a 80-Mbit/s orthogonal frequency division multiplexing (OFDM)-based nonlinear VLC system, comparable bit-error rate (BER) performance can be achieved by the conventional time domain averaging (TDA)-based LED nonlinearity mitigation scheme with totally 20 training symbols (TSs) and the proposed PBL-based scheme with only a single TS. Therefore, compared with the conventional TDA scheme, the proposed PBL-based scheme can substantially reduce the required training overhead and hence greatly improve the overall spectral efficiency of bandlimited VLC systems. It is also shown that the PBL-based LED nonlinearity estimation and compensation scheme is computational efficient for the implementation in practical VLC systems.
ISSN: 2076-3417
DOI: 10.3390/app9132711
Rights: © 2019 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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