Nonlinear Impairment Compensation Using Neural Networks

Shinsuke Fujisawa; Fatih Yaman; Hussam G. Batshon; Massaki Tanio; Naoto Ishii; Chaoran Huang; Thomas Ferreira de Lima; Yoshihisa Inada; Paul R. Prucnal; Norifumi Kamiya; Ting Wang

doi:10.1364/OFC.2021.M5F.1

Optical Fiber Communication Conference (OFC) 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper M5F.1
•https://doi.org/10.1364/OFC.2021.M5F.1

Nonlinear Impairment Compensation Using Neural Networks

Shinsuke Fujisawa, Fatih Yaman, Hussam G. Batshon, Massaki Tanio, Naoto Ishii, Chaoran Huang, Thomas Ferreira de Lima, Yoshihisa Inada, Paul R. Prucnal, Norifumi Kamiya, and Ting Wang

Optical Fiber Communication Conference 2021

Washington, DC United States
6–11 June 2021
ISBN: 978-1-943580-86-6

From the session
Machine Learning for Optical Subsystems (M5F)

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S. Fujisawa, F. Yaman, H. G. Batshon, M. Tanio, N. Ishii, C. Huang, T. F. de Lima, Y. Inada, P. R. Prucnal, N. Kamiya, and T. Wang, "Nonlinear Impairment Compensation Using Neural Networks," in Optical Fiber Communication Conference (OFC) 2021, P. Dong, J. Kani, C. Xie, R. Casellas, C. Cole, and M. Li, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper M5F.1.

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Abstract

Neural networks are attractive for nonlinear impairment compensation applications in communication systems. In this paper, several approaches to reduce computational complexity of the neural network-based algorithms are presented.

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