In this work, a perturbation-based neural network (P-NN) scheme with an embedded
bidirectional long short-term memory (biLSTM) layer is investigated to compensate for the …

Derived from the regular perturbation treatment of the nonlinear Schrödinger equation, a
machine learning-based scheme to mitigate the intra-channel optical fiber nonlinearity is …

Fiber Kerr nonlinearity is a fundamental limitation to the achievable capacity of long-distance
optical fiber communication. Digital back-propagation (DBP) is a primary methodology to …

In this paper, we review the recent progress of neural network-based Kerr nonlinearity
mitigation techniques in high-speed coherent optical fiber transmission systems. Current …

In this paper, we propose and demonstrate a low-complexity fiber nonlinearity impairments
compensation (NLC) scheme based on simple recurrent neural network (SRNN), combining …

The first-order (FO) perturbation theory-based nonlinearity compensation (PB-NLC)
technique has been widely investigated to combat the detrimental effects of the intra …

This paper investigates several novel machine learning procedures that employ two
machine learning stages to mitigate nonlinearity in dual polarized optical fiber systems …

Toward reduced-complexity digital implementation, frequency domain Volterra-based
nonlinear equalization (VNLE) structures for multistep fiber nonlinearity compensation are …

Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance
fiber optic transmission systems. Nonlinear impairments are determined by the signal …

In this work, we propose and experimentally demonstrate a novel low-complexity technique
for fiber nonlinearity compensation. We achieved a transmission distance of 2818 km for a …