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.

中文翻译：

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机器学习两阶段光纤非线性缓解

本文研究了几种新颖的机器学习程序，这些程序采用两个机器学习阶段来减轻双偏振光纤系统中的非线性。它们在发射器处采用神经网络预补偿器，在接收器处采用分类器。在接收器上研究了不同类型的分类器，例如神经网络和决策树分类器，以及许多集成方法，包括增强、随机森林和额外树。这里发现额外的树分类器产生最大的 Q 因子，增强约 1.3 dB，训练计算时间最短。