Time-Delay Twin Support Vector Regression- Based Adaptive Predistorter for LED Nonlinearity in Visible Light Communications

Abstract

The bit error rate (BER) of visible light communications (VLC) based on light emitting diodes (LEDs) has been limited due to the nonlinearity of LED. Artificial neural networks (ANNs) predistorters were applied to mitigate the impacts of LED nonlinearity. However, none of them can efficiently approach the performance of linear VLC systems due to the overfitting issue. In this paper, we for the first time propose a predistorter with adaptive indirect learning architecture based on amplitude time-delay twin support vector regression (ATD-TSVR) for LED nonlinearity in orthogonal frequency division multiplexing(OFDM)-based VLC systems. The experimental results demonstrate the signal distortion caused by LEDs as the major nonlinear source. Moreover, the simulation results show superior BER, amplitude distortion (AM/AM) curves, Power Spectral Density(PSD) and constellation plots in an 80Mbit/s OFDM nonlinear VLC system. Meanwhile, as compared to the traditional SVR approach, the CPU training time of ATD-TSVR can be reduced by more than four times. The adaptive pre-distortion method herein is generally applicable to broadband VLC systems and also proves the application prospect and effectiveness of TSVR in VLC system.