Convolutional Neural Network-Aided DP-64 QAM Coherent Optical Communication Systems

Chao Li, Yongjun Wang*, Jingjing Wang, Haipeng Yao, Xinyu Liu, Ran Gao, Leijing Yang, Hui Xu, Qi Zhang, Pengjie Ma, Xiangjun Xin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

Optical nonlinearity impairments have been a major obstacle for high-speed, long-haul and large-capacity optical transmission. In this paper, we propose a novel convolutional neural network (CNN)-based perturbative nonlinearity compensation approach in which we reconstruct a feature map with two channels that rely on first-order perturbation theory and build a classifier and a regressor as a nonlinear equalizer. We experimentally demonstrate the CNN equalizer in 375 km 120-Gbit/s dual-polarization 64-quadrature-amplitude modulation (64-QAM) coherent optical communication systems. We studied the influence of the dropout value and nonlinear activation function on the convergence of the CNN equalizer. We measured the bit-error-ratio (BER) performance with different launched optical powers. When the channel size is 11, the optimum BER for the CNN classifier is 0.0012 with 1 dBm, and for the CNN regressor, it is 0.0020 with 0 dBm; the BER can be lower than the 7$\%$ hard decision-forward threshold of 0.0038 from -3 dBm to 3 dBm. When the channel size is 15, the BERs at -4 dBm, 4 dBm and 5 dBm can be lower than 0.0020. The network complexity is also analyzed in this paper. Compared with perturbative nonlinearity compensation using a fully connected neural network (2392-64-64), we can verify that the time complexity is reduced by about 25$\%$, while the space complexity is reduced by about 50$\%$.

Original languageEnglish
Pages (from-to)2880-2889
Number of pages10
JournalJournal of Lightwave Technology
Volume40
Issue number9
DOIs
Publication statusPublished - 1 May 2022

Keywords

  • Optical fiber nonlinearity compensation
  • convolutional neural network
  • nonlinear signal distortion
  • perturbation-based nonlinearity compensation

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