Add to ORKGSeveral machine learning inspired methods for perturbation-based fiber nonlinearity (PBNLC) compensation have been presented in recent literature. We critically revisit acclaimed benefits of those over non-learned methods. Numerical results suggest that learned linear processing of perturbation triplets of PB-NLC is preferable over feedforward neural-network solutions
Nonlinearity compensation in fiber optical communication systems has been for a long time considered...
We improve an inverse regular perturbation (RP) model using a machine learning (ML) technique. The p...
In this work, we propose to use various artificial neural network (ANN) structures for modeling and ...
Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmis...
For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: f...
Nowadays, optical communication transmission is based mainly on optical fiber networks. Increasing ...
We propose a modification of the conventional perturbation-based approach of fiber nonlinearity comp...
We propose a data augmentation technique to improve performance and decrease complexity of the super...
Successful compensation of nonlinear distortions due to fiber Kerr nonlinearities relies on the avai...
We propose a perturbation-based receiver-side machine-learning equalizer for inter- and intra-channe...
Nonlinearity compensation in fiber optical communication systems has been for a long time considered...
We improve an inverse regular perturbation (RP) model using a machine learning (ML) technique. The p...
In this work, we propose to use various artificial neural network (ANN) structures for modeling and ...
Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmis...
For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: f...
Nowadays, optical communication transmission is based mainly on optical fiber networks. Increasing ...
We propose a modification of the conventional perturbation-based approach of fiber nonlinearity comp...
We propose a data augmentation technique to improve performance and decrease complexity of the super...
Successful compensation of nonlinear distortions due to fiber Kerr nonlinearities relies on the avai...
We propose a perturbation-based receiver-side machine-learning equalizer for inter- and intra-channe...
Nonlinearity compensation in fiber optical communication systems has been for a long time considered...
We improve an inverse regular perturbation (RP) model using a machine learning (ML) technique. The p...
In this work, we propose to use various artificial neural network (ANN) structures for modeling and ...