Research on accurate OSNR estimation and application prospects for autonomous optical networks under decoupled optical-electrical framework

HAN Yuqing; HU Qian; LV Zheqing; ZHU Xiankun; QIN Xin; SU Guangnan; GONG Yadong

doi:10.11959/j.issn.1000-0801.DXKX260301

Chinese

您当前的位置：

首页 >

文章列表页 >

Research on accurate OSNR estimation and application prospects for autonomous optical networks under decoupled optical-electrical framework

更新时间：2026-09-30

- Research on accurate OSNR estimation and application prospects for autonomous optical networks under decoupled optical-electrical framework
- Telecommunications Science (2026)
- 作者机构：
  
  1.中国电信股份有限公司研究院，北京 102209
  2.中电信智能网络科技有限公司，北京 102209
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2023YFB2905400)
- DOI：10.11959/j.issn.1000-0801.DXKX260301
  CLC： TN915;TP302
- Received：13 May 2026，
  
  Revised：2026-07-22，
  
  Accepted：20 August 2026，
- 稿件说明：
移动端阅览
HAN Yuqing, HU Qian, LV Zheqing, et al. Research on accurate OSNR estimation and application prospects for autonomous optical networks under decoupled optical-electrical framework[J/OL]. Telecommunications Science, 2026.
DOI：

HAN Yuqing, HU Qian, LV Zheqing, et al. Research on accurate OSNR estimation and application prospects for autonomous optical networks under decoupled optical-electrical framework[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260301.

摘要

在光电解耦架构下，光信噪比（optical signal-to-noise ratio，OSNR）评估面临精度不足、跨厂商场景适配困难和维护过度依赖人工等问题。本文围绕自治光网络中的OSNR精准评估技术开展研究，并提出配套智能管控方案。基于人工智能（artificial intelligence，AI）技术，对光层与电层参数进行解耦，结合跨域自适应对齐来缓解多厂商设备间的异构偏移；构建变换器（Transformer）与类别型梯度提升树（CatBoost）混合OSNR智能评估模型，实现OSNR高精度估计。在此基础上，将评估模型嵌入自治闭环机制，形成覆盖“数据获取—OSNR预测—异常判定—配置优化—迭代收敛—报告输出”的全流程自治体系。基于现网环境的实验结果显示，该模型OSNR评估的平均绝对误差（mean absolute error

MAE）为0.381 dB，相比传统耦合模型精度提升78.99%，验证了该方法在光电解耦架构下支撑自治光网络智能管控的可行性。

Abstract

In optical-electrical decoupled architectures

OSNR estimation methods suffer from inadequate accuracy

poor cross-vendor scenario adaptability

and heavy reliance on manual maintenance. This paper investigates precise OSNR assessment techniques for autonomous optical networks and proposes a corresponding intelligent management and control scheme. Drawing on AI technologies

we first decouple the optical-layer and electrical-layer parameters

and introduce cross-domain adaptive alignment to mitigate heterogeneous shifts across equipment from different vendors. A hybrid OSNR estimation model combining Transformer and CatBoost is then constructed to achieve high-accuracy estimation. By embedding this model into an autonomous closed-loop mechanism

we establish a full-process autonomous system

covering data acquisition

OSNR prediction

anomaly identification

configuration optimization

iterative convergence

and report generation. Experimental results from a live network environment show that the model achieves an MAE of 0.381 dB in OSNR estimation

corresponding to a 78.99% improvement in estimation accuracy over the conventional coupled model

thus verifying the feasibility of this approach for supporting intelligent management and control of autonomous optical networks under an optical-electrical decoupled architecture.

关键词

Keywords

references

FABREGA J M , et al . Experimental demonstration of a metro area network with terabit-capable sliceable bitrate variable transceiver using direct modulated VCSELs and coherent detection [C ] // Proceedings of 2022 Optical Fiber Communications Conference and Exhibition (OFC) . Piscataway : IEEE Press , 2022 : 1 - 3 .

JIANG X T , QIN X , et al . Grey-box digital twin model for QoT estimation in field-deployed networks [C ] // Proceedings of 2026 Optical Fiber Communication Conference (OFC) . Piscataway : IEEE Press , 2026 : 1 - 3 .

BORRACCINI G , et al . Experimental demonstration of partially disaggregated optical network control using the physical layer digital twin [J ] . IEEE Transactions on Network and Service Management , 2023 , 20 ( 3 ): 2343 - 2355 .

D'AMICO A , et al . Enhancing lightpath QoT computation with machine learning in partially disaggregated optical networks [J ] . IEEE Open Journal of the Communications Society , 2021 , 2 : 564 - 574 .

DONG Z , et al . Optical performance monitoring: A review of current and future technologies [J ] . Journal of Lightwave Technology , 2016 , 34 ( 2 ): 525 - 543 .

MORETTE N , et al . Machine learning enhancement of a digital twin for wavelength division multiplexing network performance prediction leveraging quality of transmission parameter refinement [J ] . Journal of Optical Communications and Networking , 2023 , 15 ( 6 ): 333 - 343 .

PAOLUCCI F , et al . Peer-to-peer disaggregated telemetry for autonomic machine-learning-driven transceiver operation [J ] . Journal of Optical Communications and Networking , 2022 , 14 ( 8 ): 606 - 620 .

LI J J , et al . Reservoir computing for few-mode fiber channel OSNR monitoring in deep learning frameworks [J ] . IEEE Photonics Journal , 2025 , 17 ( 2 ): 1 - 7 .

AHSAN M M , et al . Effect of data scaling methods on machine learning algorithms and model performance [J ] . Technologies , 2021 , 9 ( 3 ): 52 .

杨寒雨 , 赵晓永 , 王磊 . 数据归一化方法综述 [J ] . 计算机工程与应用 , 2023 , 59 ( 3 ).

YANG H Y , ZHAO X Y , WANG L . Review of data normalization methods [J ] . Computer Engineering and Applications , 2023 , 59 ( 3 ).

ALHADIDI T I , et al . Enhancing pavement crack classification with bidirectional cascaded neural networks [C ] // Proceedings of 2025 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) . Piscataway : IEEE Press , 2025 : 1 - 6 .

VASWANI A , et al . Attention is all you need [C ] // Proceedings of 2017 Neural Information Processing Systems (NIPS) . Long Beach : Curran Associates Inc. , 2017 .

GRETTON A , et al . A kernel two-sample test [J ] . Journal of Machine Learning Research , 2012 , 13 : 723 - 773 .

PROKHORENKOVA L , et al . CatBoost: unbiased boosting with categorical features [C ] // Proceedings of the 32nd International Conference on Neural Information Processing Systems (NeurIPS) . Piscataway : IEEE Press , 2018 : 6639 - 6649 .

CHINA COMMUNICATIONS STANDARDS ASSOCIATION . General technical requirements for optical wavelength division multiplexing systems : YD/T 3391-2018 [S ] . 2018 .

CHINA COMMUNICATIONS STANDARDS ASSOCIATION . Technical specification for optical safe procedure for Wavelength Division Multiplexing (WDM)system : YD/T 1259-2003 [S ] . 2003 .

叶胤 , 汪令全 . 骨干光网络工程系统性能指标分析 [J ] . 邮电设计技术 , 2022 ( 11 ): 65 - 69 .

YE Y , WANG L Q . Analysis of system performance indexes in backbone optical network engineering [J ] . Designing Techniques of Posts and Telecommunications , 2022 ( 11 ): 65 - 69 .

WANG Z , et al . Integrated sensing and communication (ISAC) in optical fiber networks: Current research and challenges [C ] // Proceedings of 2025 International Conference on Advanced Information and Communication Technology (ICAIT) . Piscataway : IEEE Press , 2025 : 1 - 5 .

BAO T , et al . Joint optimization of DNN model partitioning and slice delivery for distributed edge-cloud inference over optical networks [J ] . Journal of Optical Communications and Networking , 2025 , 17 ( 11 ): 1047 - 1058 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

VisionTS 时序预测在电信智能决策辅助系统中的应用与实现

TAPFormer: 面向LLM的工具调用性能预测

可信数据空间在医疗行业应用的研究与探索

WAN-RDMA广域无损应用于入算专线场景的组网技术探索

基于M-LAG技术的天翼云专线改造方案

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰