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Article

LED Nonlinearity Estimation and Compensation in VLC Systems Using Probabilistic Bayesian Learning

1
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
2
Department of Electrical Engineering, Eindhoven University of Technology (TU/e), Flux Building, 5600MB Eindhoven, The Netherlands
3
College of Computer Science, Sichuan University, Chengdu 610065, China
4
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2711; https://doi.org/10.3390/app9132711
Received: 27 May 2019 / Revised: 20 June 2019 / Accepted: 29 June 2019 / Published: 3 July 2019
(This article belongs to the Special Issue Optics for AI and AI for Optics)
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. View Full-Text
Keywords: light emitting diode; nonlinearity estimation and compensation; probabilistic Bayesian learning; visible light communication light emitting diode; nonlinearity estimation and compensation; probabilistic Bayesian learning; visible light communication
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MDPI and ACS Style

Chen, C.; Deng, X.; Yang, Y.; Du, P.; Yang, H.; Zhao, L. LED Nonlinearity Estimation and Compensation in VLC Systems Using Probabilistic Bayesian Learning. Appl. Sci. 2019, 9, 2711. https://doi.org/10.3390/app9132711

AMA Style

Chen C, Deng X, Yang Y, Du P, Yang H, Zhao L. LED Nonlinearity Estimation and Compensation in VLC Systems Using Probabilistic Bayesian Learning. Applied Sciences. 2019; 9(13):2711. https://doi.org/10.3390/app9132711

Chicago/Turabian Style

Chen, Chen; Deng, Xiong; Yang, Yanbing; Du, Pengfei; Yang, Helin; Zhao, Lifan. 2019. "LED Nonlinearity Estimation and Compensation in VLC Systems Using Probabilistic Bayesian Learning" Appl. Sci. 9, no. 13: 2711. https://doi.org/10.3390/app9132711

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