Machine-learning-based technique to establish ASE or Kerr impairment dominance in optical transmission

Data extraction from optical networks has increased substantially with the evolution of monitoring and telemetry methods. Using data analysis and machine learning, this paper aims to derive insights from this data, contributing to the development of self-optimized optical networks. More particularly, it focuses on predicting the Kerr and amplified spontaneous emission dominance by examining the fluctuations in the signal-to-noise ratio due to polarization-dependent loss. Building on previous work, which used the SNR statistic as the input feature of machine learning, our primary goal is to enhance prediction precision while concurrently decreasing the computational model's complexity. After refining the selection parameters of the input features, we observed a 70% reduction in the input feature length with respect to our previous work. The model reached a 98% accuracy rate, and it was able to successfully classify the regimes in a limited set of unseen experimental instances.