Simplifying the Supervised Learning of Kerr Nonlinearity Compensation Algorithms by Data Augmentation

We propose a data augmentation technique to improve performance and decrease complexity of the supervised learning of nonlinearity compensation algorithms. We demonstrate both numerically and experimentally that the augmentation allows reducing the training dataset size up to 6 times while keeping the same post-compensation bit-error rate.