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Machine learning two stage optical fiber nonlinearity mitigation

This paper investigates several novel machine learning procedures that employ two machine learning stages to mitigate nonlinearity in dual polarized optical fiber systems. These employ a neural network pre-compensator at the transmitter and a classifier at the receiver. Different types of classifiers such as neural network and decision tree classifiers as well as a number of ensemble methods including boosting, random forest, and extra trees are investigated at the receiver. Here the extra trees classifier is found to yield the greatest Q-factor with ∼1.3 dB enhancement and lowest training computational time.