Research on Failure Localization Strategies in High-Degree Optical Switching Networks

WANG Ruikun; ZHANG Jiawei; SONG Yuqing; ZHANG Qiaolun; WANG Jingjing

doi:10.13756/j.gtxyj.2025.250275

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Research on Failure Localization Strategies in High-Degree Optical Switching Networks

更新时间：2025-12-25

- Research on Failure Localization Strategies in High-Degree Optical Switching Networks
  增强出版
- Study on Optical Communications Issue 6, (2025)
- 作者机构：
  
  1.青岛科技大学　信息科学技术学院&山东省深海装备智联网重点实验室，青岛　266061
  2.北京邮电大学　信息光子学与光通信全国重点实验室，北京　100876
  3.米兰理工大学　电气、电子与生物工程系，意大利　米兰　20133
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2025.250275
  CLC： TN915
- Received：01 August 2025，
  
  Revised：2025-09-03，
  
  Published：10 December 2025
- 稿件说明：
移动端阅览
王瑞坤，张佳玮，宋宇晴，等. 高维度光交换网络的故障定位策略研究[J]. 光通信研究，2025(6): 250275.

Wang R K, Zhang J W, Song Y Q, et al. Research on Failure Localization Strategies in High-Degree Optical Switching Networks[J]. Study on Optical Communications, 2025(6): 250275.
王瑞坤，张佳玮，宋宇晴，等. 高维度光交换网络的故障定位策略研究[J]. 光通信研究，2025(6): 250275. DOI： 10.13756/j.gtxyj.2025.250275.

Wang R K, Zhang J W, Song Y Q, et al. Research on Failure Localization Strategies in High-Degree Optical Switching Networks[J]. Study on Optical Communications, 2025(6): 250275. DOI： 10.13756/j.gtxyj.2025.250275.

摘要

【目的】

为了研究高维度光交换网络的故障定位问题，实现交换节点内部和通信链路中光器件级别的故障准确定位，为智能光网络运维提供有效技术支撑，文章提出了一种基于级联神经网络（Ca-NN）模型的故障定位算法。

【方法】

文章对高维度节点架构和组网特性进行了分析，设计了模型回溯策略来解决故障定位错误的问题，引入蚁群算法实现了光性能遥测（OPM）的高效部署，并在5节点和8条双向链路的网络拓扑下进行了数值评估。

【结果】

仿真实验结果表明，文章所提算法在不同光器件类型、不同OPM部署比例下都获得了较传统算法更高的故障定位准确度。

【结论】

文章所提算法在处理高维度光交换网络的故障定位问题时，具备故障高准确度定位和有效纠错等能力，该算法可为大容量和高可靠光网络的运维提供有效技术支撑。

Abstract

【Objective】

To address fault localization in high-dimensional optical switching networks—achieving precise identification of failures at the optical-device level both inside switching nodes and along communication links

and thereby providing effective technical support for the operation and maintenance of intelligent optical networks—this paper proposes a failure localization algorithm based on a Cascaded Neural Network (Ca-NN) model.

【Methods】

The paper analyzes the network architecture and its networking characteristics

designs a model backtracking strategy to address failure localization errors

and introduces the ant colony algorithm to achieve efficient deployment of the Optical Performance Monitor (OPM). Finally

numerical evaluations are conducted on a network topology consisting of 5 nodes and 8 bidirectional links.

【Results】

The simulation results demonstrate that the proposed algorithm achieves higher failure localization accuracy compared to traditional algorithms under various optical component types and different deployment ratios of OPMs.

【Conclusion】

The proposed algorithm is specifically designed to address the failure localization problem in high-degree optical switching networks

featuring high localization accuracy and effective error correction capabilities. The developed algorithm provides effective technical support for the operation and maintenance of large-capacity and highly reliable optical networks.

关键词

Keywords

references

张佳玮 , 苏琛 , 纪越峰 . 工业PON中确定性网络传输技术研究(特邀) [J ] . 光通信研究 , 2024 ( 1 ): 230168 .

Zhang J W , Su C , Ji Y F . Research on Deterministic Network Transmission Technology in Industrial PON [J ] . Study on Optical Communications , 2024 ( 1 ): 230168 .

Cheng Z , Ye T , Zhu Y , et al . A Three-Phase Modularization Approach of OXC for Large-Scale ROADM [J ] . Journal of Lightwave Technology , 2023 , 41 ( 24 ): 7318 - 7327 .

Mehrvar H , Li S , Bernier E . Dimensioning Networks of High Degree ROADMs [C ] // 2022 European Conference on Optical Communication (ECOC) . Basel, Switzerland : IEEE , 2022 : 9979258 .

高轶丰 , 孟二帅 , 戴瑄辰 , 等 . 环回互连数据中心光网络(特邀) [J ] . 光通信研究 , 2024 ( 5 ): 240027 .

Gao Y F , Meng E S , Dai X C , et al . Loopback Interconnect Datacenter Optical Network [J ] . Study on Optical Communications , 2024 ( 5 ): 240027 .

李鸿 , 刘武 , 罗鸣 . 基于机器学习的光网络监测与优化方法 [J ] . 光通信研究 , 2024 ( 3 ): 230046 .

Li H , Liu W , Luo M . Optical Network Monitoring and Optimization Methods based on Machine Learning [J ] . Study on Optical Communications , 2024 ( 3 ): 230046 .

Behera S , Panayiotou T , Ellinas G . Machine Learning Framework for Timely Soft-Failure Detection and Localization in Elastic Optical Networks [J ] . Journal of Optical Communications and Networking , 2023 , 15 ( 10 ): E74 - E85 .

Zhao X , Yang H , Guo H , et al . Accurate Fault Location based on Deep Neural Evolution Network in Optical Networks for 5G and beyond [C ] // 2019 Optical Fiber Communications Conference and Exhibition (OFC) . San Diego, CA, USA : IEEE , 2019 : 8696344 .

Mayer K S , Soares J A , Pinto R P , et al . Machine-Learning-based Soft-Failure Localization with Partial Software-Defined Networking Telemetry [J ] . Journal of Optical Communications and Networking , 2021 , 13 ( 10 ): E122 - E131 .

Wang R , Zhang J , Gu Z , et al . Digital-Twin-Assisted Meta Learning for Soft-Failure Localization in ROADM-based Optical Networks [J ] . Journal of Optical Communications and Networking , 2024 , 16 ( 7 ): C11 - C19 .

Song Y , Zhang J , Wang R , et al . Cascaded-Neural-Networks-Enabled Failure Localization in High-Degree Clos-ROADM Optical Networks [C ] // 2024 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC) . Beijing, China : IEEE , 2024 : 10809410 .

Wang R , Zhang Q , Zhang J , et al . Multi-Failure Localization in High-Degree ROADM-based Optical Networks Using Rules-Informed Neural Networks [J ] . IEEE Journal on Selected Areas in Communications , 2025 , 43 ( 5 ): 1738 - 1754 .

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