Machine Learning based OTN Network Performance Degradation Prediction
【Objective】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 c...
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| Format: | Article |
| Language: | zho |
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《光通信研究》编辑部
2025-04-01
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| Series: | Guangtongxin yanjiu |
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| Online Access: | http://www.gtxyj.com.cn/zh/article/doi/10.13756/j.gtxyj.2025.240047/ |
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| _version_ | 1849729176990384128 |
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| author | CHEN Liping LIAO Liang ZHANG Peng ZHU Dehan PENG Zhichong ZHOU Hao |
| author_facet | CHEN Liping LIAO Liang ZHANG Peng ZHU Dehan PENG Zhichong ZHOU Hao |
| author_sort | CHEN Liping |
| collection | DOAJ |
| description | 【Objective】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.【Methods】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.【Results】Verified through simulation experiments, the prediction accuracy of this method exceeds 90%.【Conclusion】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. |
| format | Article |
| id | doaj-art-6a4fb182ef324c5fb69477cf7c907fa9 |
| institution | DOAJ |
| issn | 1005-8788 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | 《光通信研究》编辑部 |
| record_format | Article |
| series | Guangtongxin yanjiu |
| spelling | doaj-art-6a4fb182ef324c5fb69477cf7c907fa92025-08-20T03:09:18Zzho《光通信研究》编辑部Guangtongxin yanjiu1005-87882025-04-01240047-0690716824Machine Learning based OTN Network Performance Degradation PredictionCHEN LipingLIAO LiangZHANG PengZHU DehanPENG ZhichongZHOU Hao【Objective】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.【Methods】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.【Results】Verified through simulation experiments, the prediction accuracy of this method exceeds 90%.【Conclusion】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.http://www.gtxyj.com.cn/zh/article/doi/10.13756/j.gtxyj.2025.240047/OTNframe error probability predictionoptical channel BER predictionlong short-term memorylogistic regression |
| spellingShingle | CHEN Liping LIAO Liang ZHANG Peng ZHU Dehan PENG Zhichong ZHOU Hao Machine Learning based OTN Network Performance Degradation Prediction Guangtongxin yanjiu OTN frame error probability prediction optical channel BER prediction long short-term memory logistic regression |
| title | Machine Learning based OTN Network Performance Degradation Prediction |
| title_full | Machine Learning based OTN Network Performance Degradation Prediction |
| title_fullStr | Machine Learning based OTN Network Performance Degradation Prediction |
| title_full_unstemmed | Machine Learning based OTN Network Performance Degradation Prediction |
| title_short | Machine Learning based OTN Network Performance Degradation Prediction |
| title_sort | machine learning based otn network performance degradation prediction |
| topic | OTN frame error probability prediction optical channel BER prediction long short-term memory logistic regression |
| url | http://www.gtxyj.com.cn/zh/article/doi/10.13756/j.gtxyj.2025.240047/ |
| work_keys_str_mv | AT chenliping machinelearningbasedotnnetworkperformancedegradationprediction AT liaoliang machinelearningbasedotnnetworkperformancedegradationprediction AT zhangpeng machinelearningbasedotnnetworkperformancedegradationprediction AT zhudehan machinelearningbasedotnnetworkperformancedegradationprediction AT pengzhichong machinelearningbasedotnnetworkperformancedegradationprediction AT zhouhao machinelearningbasedotnnetworkperformancedegradationprediction |