56-Gbit/s PAM-4 Optical Signal Transmission Over 100-km SMF Enabled by TCNN Regression Model

Abstract

Due to the interaction between chromatic dispersion (CD) and direct detection, the CD induced power-fading effect has been considered as the key point to limit transmission rate and distance in intensity modulation direct-detection (IM/DD) systems. Besides, the performance of the IM/DD systems will be further affected by the bandwidth limitation and nonlinearity effect. In this paper, we propose a low complexity nonlinear equalizer (NLE) based on temporal convolutional neural network (TCNN) regression model which combines dilated convolutions with residual connections. Then, the performance of our proposed equalizer is experimentally demonstrated in 56-Gbit/s 4-level pulse amplitude modulation (PAM4) intensity modulation direct-detection system. The results show that the receiver sensitivity with the help of our TCNN equalizer can be improved about 1.5 dB and 3.5 dB compared to 2D-CNN and 1D-CNN equalizer under 70-km single mode fiber (SMF) transmission. Furthermore, the proposed equalizer can extend maximum transmission distance to 100 km at bit error rate threshold of 3.8 × 10−3.

Keywords

• Convolutional neural network
• direct detection
• intensity modulation
• short-reach transmission link