Machine Learning Based Optical Transmission System Link Performance Degradation Prediction and Application

Optical networks play a critical role in modern information society. Statistics have revealed that around 50% of optical service faults stem from "errors" in the optical transmission system links, which are monitored through frame overheads. Unfortunately, current maintenance methods are insufficient in accurately predicting these errors, leading to potential disruptions in network services. To address this issue, this study introduces the Gini importance-based feature selection approach and maximum likelihood-based logistic regression algorithm for predicting optical transmission system link performance degradation. To facilitate the application of this model in real-world networks, the Network AI (NAI) three-level intelligent architecture is proposed, and an optical transmission performance degradation prediction system is developed based on this architecture. Experimental results from the network experiment demonstrate the effectiveness of the proposed performance degradation model and NAI-based system.