光通信研究Issue(2)：46-51,6.DOI:10.13756/j.gtxyj.2025.240047
基于机器学习的OTN网络性能劣化预测
Machine Learning based OTN Network Performance Degradation Prediction
摘要
Abstract
[Objective]This paper aims to address the challenge of predicting performance degradation(frame transmission er-rors)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 ma-jority 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.[Methods]This paper proposes a creative approach to predict error probability by leveraging the correspondence be-tween 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.[Results]Verified through simulation experiments,the predic-tion accuracy of this method exceeds 90%.[Conclusion]The proposed solution meets the requirements for engineering applica-tions,providing a new and effective method for predicting performance degradation in OTN networks.It also provides a strong ba-sis for predictive maintenance of OTN networks.关键词
光传送网/帧误码概率预测/光信道误码率预测/长短期记忆网络/逻辑回归Key words
OTN/frame error probability prediction/optical channel BER prediction/long short-term memory/logistic regres-sion分类
电子信息工程引用本文复制引用
陈丽萍,廖亮,张鹏,朱德瀚,彭智聪,周浩..基于机器学习的OTN网络性能劣化预测[J].光通信研究,2025,(2):46-51,6.基金项目
湖北省重点研发计划资助项目(2024BAB029) （2024BAB029）
