Machine Learning for Fiber Nonlinearity Mitigation in Long-Haul Coherent Optical Transmission Systems : Invited Paper

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learn...

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Bibliographic Details
Published in2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT) pp. 124 - 127
Main Authors Liu, Yifan, Yang, Bowei, Xu, Tianhua
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Fiber nonlinear optics
machine learning
nonlinearity compensation
optical communications and networking
Optical fiber networks
Optical fibers
Support vector machines
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Summary:Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation.
DOI:10.1109/ICAIT.2019.8935891