Chinese Optics Letters, Volume. 23, Issue 5, 050601(2025)

Temporal feature-based memory neural network for probabilistic-shaping polarization-division multiplexed ultrahigh-order QAM coherent optical transmission

Xuejing Huang, Mingyi Gao*, Jiamin Fan, Yifan Ge, Xiaodi You, and Gangxiang Shen
Author Affiliations
  • Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network, Suzhou Key Laboratory of Advanced Optical Communication Network Technology, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
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    References(18)

    [1] M. Terayama, S. Okamoto, K. Kasai et al. 4096 QAM (72 Gbit/s) single-carrier coherent optical transmission with a potential SE of 15.8 bit/s/Hz in all-raman amplified 160 km fiber link. 2018 Optical Fiber Communications Conference and Exposition (OFC), 1(2018).

    [12] S. Bai, J. Kolter, V. Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modelingar(2018).

    [13] C. Cortes, M. Mohri, A. Rostamizadeh. L2 regularization for learning kernels(2012).

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    Xuejing Huang, Mingyi Gao, Jiamin Fan, Yifan Ge, Xiaodi You, Gangxiang Shen, "Temporal feature-based memory neural network for probabilistic-shaping polarization-division multiplexed ultrahigh-order QAM coherent optical transmission," Chin. Opt. Lett. 23, 050601 (2025)

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Oct. 21, 2024

    Accepted: Nov. 29, 2024

    Published Online: Apr. 30, 2025

    The Author Email: Mingyi Gao (mygao@suda.edu.cn)

    DOI:10.3788/COL202523.050601

    CSTR:32184.14.COL202523.050601

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