Abstract
A novel learned modified perturbation backpropagation (L-MPBP) algorithm for high-symbol-rate (HSR) coherent optical transmission system is proposed in this paper. As an extension of perturbation backpropagation (PBP), L-MPBP utilizes deep neural network to adjust the position of nonlinear compensation (NLC) in each compensation step and jointly optimize the perturbation coefficients, which greatly improves the performance of nonlinear equalization in HSR transmission systems. The proposed approach is applied to compensate nonlinear signal distortions in a 90-GBaud, PDM-16QAM coherent transmission system. The results show that the proposed L-MPBP achieves 1.75 dB effective SNR improvement compared with PBP while without inducing extra complexity. Our research indicates that the L-MPBP algorithm has great application potential for nonlinear compensation in HSR systems.
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