Machine learning for long-haul optical systems

Kapitel i bok, 2022

In this chapter, machine learning (ML) algorithm is introduced in single-step perturbation and multi-step digital back-propagation methods to compensate for intra-channel fiber nonlinearity. A wide & deep neural network is first introduced to use intra-channel cross-phase modulation and intra-channel four-wave mixing triplets as input features to predict the fiber nonlinearity at symbol rate. In the second part, a parameterized physics-based ML model is reviewed by emulating linear dispersion step as the weight matrices in a neural network and nonlinear phase shift as element-wise nonlinearities in the activation functions. The chapter concludes by a short summary outlook of ML in long-haul systems.