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.
|Name||2020 European Conference on Optical Communications, ECOC 2020|
|Conference|| 2020 European Conference on Optical Communications|
|Abbreviated title||ECOC 2020|
|Period||6/12/20 → 10/12/20|
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