Fiber nonlinearity effects cap achievable rates and ranges in long-haul optical fiber
communication links. Conventional nonlinearity compensation methods, such as …

Abstract Machine learning (ML) is being hailed as a new direction of innovation to transform
future optical communication systems. Signal processing paradigms based on ML are being …

A normalized batch gradient descent optimizer is proposed to improve the first-order regular
perturbation coefficients of the Manakov equation, often referred to as kernels. The …

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

By combining the nonlinear impairment features derived from the first-order perturbation
theory, we propose a nonlinear impairment compensation (NLC) scheme based on the …

We propose an approach for constraining the set of nonlinear coefficients of the
conventional first-order regular perturbation (FRP) model of the Manakov Equation. We …

An increasing number of scholars have proposed many schemes to mitigate the Kerr
nonlinearity effect restricting the transmission capacity of optical fibres. In this paper, we …

We experimentally demonstrate the maximum 1.07 dB Q-factor improvement in terms of
nonlinearity compensation of 120 Gb/s 64-QAM coherent optical communication system at …

We propose and demonstrate an intrachannel fiber nonlinear impairments compensation
scheme based on the transformer and simplified triplets. In comprehensive numerical …

Several machine learning inspired methods for perturbation-based fiber n onlinearity
(PBNLC) compensation have been presented in recent literature. We critically revisit …