Learning for Perturbation-Based Fiber Nonlinearity Compensation

2022/10/07 by Luo, Shenghang, Soman, Sunish Kumar Orappanpara, Lampe, Lutz +2
#FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2210.03440

Abstract

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.

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