This paper aims to address the challenge of predicting performance degradation (frame transmission errors) in Optical Transport Network (OTN). Frame error performance metrics in OTN rely on the detection of Bit Interleaved Parity (BIP) bytes in OTN frame overhead, which are periodically calculated by network management systems. In the vast majority of cases where the OTN network operates normally, the error-related performance values remain zero, which undoubtedly poses a challenge for both traditional methods and the Artificial Intelligence (AI) technologies in predicting OTN error-related performance.

This paper proposes a creative approach to predict error probability by leveraging the correspondence between the optical and electrical layers in OTN. Firstly, deep learning techniques are used to predict the trend of Bit Error Rates (BER) in optical channels. Subsequently, based on the predicted BER in optical channels, the proposed machine learning models are employed to further predict the frame error probability in OTN.

Verified through simulation experiments, the prediction accuracy of this method exceeds 90%.

The proposed solution meets the requirements for engineering applications, providing a new and effective method for predicting performance degradation in OTN networks. It also provides a strong basis for predictive maintenance of OTN networks.