Learning for Perturbation-Based Fiber Nonlinearity Compensation (2210.03440v1)

Published 7 Oct 2022 in eess.SP

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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