Simplifying the supervised learning of kerr nonlinearity compensation algorithms by data augmentation
2020 European Conference on Optical Communications (ECOC), 2020•ieeexplore.ieee.org
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.
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.
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.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果