A Survey of Neural Network Applications in Fiber Nonlinearity Mitigation

Advance in optical transmission towards higher bit rate and denser spectral efficiency is challenged by nonlinear effects. The existing digital signal processing techniques to compensate nonlinear fiber transmission impairments suffer from heavy computations and require the knowledge of a large number of system parameters, which is impractical in field environment. Neural networks are among the investigated solutions in literature to cope with the complexity of those models. Taking advantage of the huge amount of data available in optical transport networks, WDM systems represent a fertile field to apply neural networks.This paper is a non-exhaustive survey on neural network applications for nonlinear impairments mitigation in optical fiber transmission systems. We distinguish two approaches. The first one is dependent of the NonLinear Schrödinger Equation (NLSE) while the second one is based on machine learning techniques. These two approaches achieve similar performance compared to the well-known nonlinear mitigation methods with reduced computational complexity.