Neural Network Nonlinear Equalizer in Long-Distance Coherent Optical Transmission Systems

Abstract

Recently, neural network (NN) nonlinear equalizers which are expected to reduce computational complexity have attracted attention as fiber nonlinear compensation methods for coherent optical transmission systems. However, these fiber nonlinear compensation methods have problems that training of NN is not easy for long-distance transmission systems because of accumulated phase noise. In this letter, we propose a training method of NN nonlinear equalizer using the target outputs including phase noise which is produced by received signals. The phase noise is estimated by the inverse modulation of received signals and filtering. The estimated phase noise is added to transmitter signals, and the target outputs are obtained. The target outputs allow training of NN in long-distance coherent optical transmission systems. Since the NN trained by this proposed method compensates only for fiber nonlinearities, the phase locked loop (PLL) is placed after the NN to compensate for phase noise. The performances are evaluated by the simulation of 32 Gbaud 16QAM 4000 km long-distance coherent optical transmission. These results indicate that the proposed training method is effective in training NN nonlinear equalizer in long-distance coherent optical transmission in the presence of phase noise.