Fiber Nonlinearity Compensation by Neural Networks

Publication Date: 4/8/2019

Event: SubOptic 2019

Reference: pp. 1-7, 2019

Authors: Shaoliang Zhang, NEC Laboratories America, Inc.; Fatih Yaman, NEC Laboratories America, Inc.; Takanori Inoue, NEC Corporation; Kohei Nakamura, NEC Corporation; Eduardo Mateo, NEC Corporation; Yoshihisa Inada, NEC Corporation

Abstract: Neuron network (NN) is proposed to work together with perturbation-based nonlinearity compensation (NLC) algorithm by feeding with intra-channel cross-phase modulation (IXPM) and intra-channel four-wave mixing (IFWM) triplets. Without prior knowledge of the transmission link and signal pulse shaping/baudrate, the optimum NN architecture and its tensor weights are completely constructed from a data-driven approach by exploring the training datasets. After trimming down the unnecessary input tensors based on their weights, its complexity is further reduced by applying the trained NN model at the transmitter side thanks to the limited alphabet size of the modulation formats. The performance advantage of Tx-side NN-NLC is experimentally demonstrated using both single-channel and WDM-channel 32Gbaud dual-polarization 16QAM over 2800km transmission

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