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Multi-Task Learning Aided Neural Networks for Equalization in Coherent Optical System

In this paper, we propose an NN-based multi-symbol equalization scheme inspired by multi-task learning. Compared with the traditional single-output NN-based equalizers, the computational complexity (CC) can be significantly reduced. We conduct an experiment to transmit a 128-Gb/s DP-16QAM signal transmission over 600-km standard single mode fiber. The experimental results show that a maximum CC reduction of 97% can be achieved with Bi-LSTM-based multi-symbol equalization compared with the corresponding single-output NNs.