Discrete wavelet transform assisted convolutional neural network equalizer for PAM VLC system

Abstract

With the deepening of research and the further differentiation of damage types, and to compensate for both linear and nonlinear damage in visible light communication systems (VLCs), we propose a novel discrete wavelet transform-assisted convolutional neural network (DWTCNN) equalizer that combines the advantages of wavelet transform and deep learning methods. More specifically, wavelet transform is used in DWTCNN to decompose the signal into diverse coefficient series and employ an adaptive soft-threshold method to eliminate redundant information in the signal. The coefficients are then reconstructed to achieve complete signal compensation. The experimental results show that the proposed DWTCNN equalizer can significantly reduce nonlinear impairment and improve system performance with the bit error rate (BER) under the 7% hard-decision forward error correction (HD-FEC) limit of 3.8 × 10−3. We also experimentally compared DWTCNN with the Long Short-Term Memory (LSTM) and entity extraction neural network (EXNN) equalizer, the Q factor has been improved by 0.76 and 0.53 dB, and the operating ranges of the direct current (DC) bias have increased by 4.76% and 23.5%, respectively.