Low-complexity nonlinear equalizer based on artificial neural network for 112 Gbit/s PAM-4 transmission using DML

Artificial neural network (ANN) can easily approximate a nonlinear function and has strong nonlinear repre-sentation ability. Nonlinear equalizer based on artificial neural network (ANN-NLE) has been proved to be effective in dealing with linear and nonlinear impairments in different types of short reach intensity modulation with direct detection (IM/DD) systems. However, the complexity of ANN-NLE is very high, which limits the application in low-cost direct detection systems. We propose a novel weight pruning method named dynamic pruning to reduce the complexity of ANN-NLE. We demonstrate a C-band 112 Gbit/s four-level pulse-amplitude modulation (PAM-4) system over 4 km standard single mode fiber (SSMF) transmission. The simulation results show that the dynamic pruning method and weight pruning method have similar computational complexity after pruning, and the dynamic pruning method has better performance and lower bit error rate (BER).

Automated summary

Artificial neural networks can effectively approximate nonlinear functions, but their complexity can be reduced through dynamic pruning methods. In optical fiber transmission systems, dynamic pruning methods show better performance and lower bit error rates.