Enhancing Closed-Form Based Physical Layer Performance Estimations in EONs Via Machine Learning Techniques

We show that the combination of machine learning methods and analytical expressions can enhance the OSNIR estimation of an optical link of up to 2 dB compared with the use of an analytical expression alone. For this purpose, we exploit seven machine learning algorithms and we examine their OSNIR improvement for 3,000 different operational cases.