Performance and complexity study of a neural network post-equalizer in a 638-nm laser transmission system through over 100-m plastic optical fiber

Abstract

In recent years, plastic optical fiber (POF) has been considered as a promising cost-effective scheme for short-distance data communications, multimedia communication in cars, and in-house networks. However, due to the intrinsic nature of the relatively large numerical aperture of POF and the high attenuation rate, implementing high data rates over 100-m POF transmission length will be a significant challenge. We propose a scheme of high-speed 100-m POF transmission system based on a visible red laser and a cascaded neural network (NN) post-equalizer. To mitigate the nonlinear distortion induced by the POF, three different NNs, i.e., convolutional NN (CNN), long and short-term memory NN (LSTM), and cascaded NN structure consisting of convolutional layers and LSTM (CNN-LSTM), are employed as the post-equalizer. Experimental results show that using three different post-equalizers can significantly improve the system performance compared with the Volterra equalizer baseline. Among them, CNN-LSTM can outperform the others in terms of the bit error rate (BER) and the system Q-factor in the low nonlinear region. When the system operating in strong nonlinear region, CNN can achieve optimal performance at a lower system overhead of complexity. Finally, we successfully demonstrated a 100-m POF transmission system using 16 quadrature amplitude modulation discrete Fourier transform-spread orthogonal frequency division multiplexing modulation format at 1.8 Gbps with BER below 3.8 × 10 − 3 by utilizing CNN-LSTM.