Nonlinear Equalizer by Feature Engineering Based-Deep Neural Network for Coherent Optical Communication System

Xinyu Liu; Yongjun Wang; Chao Li

doi:10.1364/ACPC.2020.M4A.275

Asia Communications and Photonics Conference/International Conference on Information Photonics and Optical Communications 2020 (ACP/IPOC)
OSA Technical Digest (Optica Publishing Group, 2020),
paper M4A.275
•https://doi.org/10.1364/ACPC.2020.M4A.275

Nonlinear Equalizer by Feature Engineering Based-Deep Neural Network for Coherent Optical Communication System

Xinyu Liu, Yongjun Wang, and Chao Li

Asia Communications and Photonics Conference 2020

Beijing China
24–27 October 2020
ISBN: 978-1-943580-82-8

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X. Liu, Y. Wang, and C. Li, "Nonlinear Equalizer by Feature Engineering Based-Deep Neural Network for Coherent Optical Communication System," in Asia Communications and Photonics Conference/International Conference on Information Photonics and Optical Communications 2020 (ACP/IPOC), K. Xu, D. Simeonidou, C. Chang-Hasnain, N. Wada, X. Ren, Z. Zhukov, P. Perry Shum, Y. Ji, J. Zhang, X. Zhou, C. Lu, and L. Wosinska, eds., OSA Technical Digest (Optica Publishing Group, 2020), paper M4A.275.

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Abstract

We experimentally demonstrate the maximum 1.07 dB Q-factor improvement in terms of nonlinearity compensation of 120 Gb/s 64-QAM coherent optical communication system at 375 km, using a feature engineering-based deep neural network (FE-DNN) nonlinear equalizer.

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