Learning for Perturbation-Based Fiber Nonlinearity Compensation

Learning for Perturbation-Based Fiber Nonlinearity Compensation

Several machine learning inspired methods for perturbation-based fiber nonlinearity (PBNLC) compensation have been presented in recent literature. We critically revisit acclaimed benefits of those over non-learned methods. Numerical results suggest that learned linear processing of perturbation trip...

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Bibliographic Details
Main Authors: Luo, Shenghang, Soman, Sunish Kumar Orappanpara, Lampe, Lutz, Mitra, Jeebak, Li, Chuandong
Format: Journal Article
Language:English
Published: 07-10-2022
Online Access:Get full text
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Summary:Several machine learning inspired methods for perturbation-based fiber nonlinearity (PBNLC) compensation have been presented in recent literature. We critically revisit acclaimed benefits of those over non-learned methods. Numerical results suggest that learned linear processing of perturbation triplets of PB-NLC is preferable over feedforward neural-network solutions.
DOI:10.48550/arxiv.2210.03440