Title: Sparse Identification for Nonlinear Optical Communication Systems: SINO Method,
Abstract: We introduce low complexity machine learning based approach for mitigating
nonlinear impairments in optical fiber communications systems. The immense
intricacy of the problem calls for the development of "smart" methodology,
simplifying the analysis without losing the key features that are important for
recovery of transmitted data. The proposed sparse identification method for
optical systems (SINO) allows to determine the minimal (optimal) number of
degrees of freedom required for adaptive mitigation of detrimental nonlinear
effects. We demonstrate successful application of the SINO method both for
standard fiber communication links and for few-mode
spatial-division-multiplexing systems.