基于细粒度星座图识别的光性能监测方法

doi:10.11896/jsjkx.220600238

摘要/Abstract

摘要： 在光纤通信中,传统光性能监测(Optical Performance Monitoring,OPM)主要依靠分析信号的时频域信息来实现,但此类方法无法完成多任务联合监测,因此其灵活性较低。随着机器学习的发展,基于机器学习的光信号调制格式(ModulationFormat,MF)及光信噪比(Optical Signal Noise Ratio,OSNR)监测方法被逐渐应用。但现有方法未考虑信号的细粒度特征,因此在复杂场景中对OSNR的监测精度较低。针对上述问题,文中提出了一种基于细粒度星座图识别的光信号MF和OSNR联合监测模型(Fine-Grained Optical Performance Monitor Network,FGNet)。首先,在骨干特征提取模块中采用深度残差结构对星座图进行深度特征提取;其次,提出多层双线性池化(Multilayer Bilinear Pooling)模块,对星座图特征进行细粒度特征分析;最后,提出联合监测模块对MF和OSNR进行特征融合分析。在拥有7 200张星座图的仿真数据集中进行广泛的实验,实验结果表明,所提方法相比现有方法取得了更优越的性能。

关键词: 机器学习, 光信噪比监测, 调制格式分类, 细粒度图像识别, 残差神经网络

Abstract: In optic fiber communication,traditional optical performance monitoring(OPM) mainly relies on analyzing the time-frequency domain information of the signal.However,conventional methods cannot complete multi-task joint monitoring,so they are less flexible.With the development of machine learning,the monitoring of optical signal modulation format(MF) and optical signal-to-noise ratio(OSNR) based on machine learning have been gradually applied.However,existing methods have low accuracy for OSNR monitoring in complex scenarios because they do not consider the fine-grained characteristics of the signal.This paper proposes a joint monitoring model(FGNet) for optical signal MF and OSNR based on fine-grained constellation identification to solve this problem.Firstly,the backbone feature extraction module uses a deep residual structure.Secondly,a multilayer bilinear pooling module is proposed to perform fine-grained feature analysis on constellation features.Finally,a joint MF and OSNR monitoring module is proposed to realize the feature fusion of MF and OSNR.Extensive experiments with 7 200 constellation maps in the simulation dataset show that the proposed model has achieved superior performance compared to existing methods.

Key words: Machine learning, OSNR monitoring, Modulation format classification, Fine-grained image recognition, Residual neural network

中图分类号:

TP389

陈进杰, 贺超, 肖枭, 雷印杰. 基于细粒度星座图识别的光性能监测方法[J]. 计算机科学, 2023, 50(4): 220-225. https://doi.org/10.11896/jsjkx.220600238

CHEN Jinjie, HE Chao, XIAO Xiao, LEI Yinjie. Optical Performance Monitoring Method Based on Fine-grained Constellation Diagram Recognition[J]. Computer Science, 2023, 50(4): 220-225. https://doi.org/10.11896/jsjkx.220600238

参考文献

[1]DONG Z,KHAN F N,SUI Q,et al.Optical performance monitoring:A review of current and future technologies[J].Journal of Lightwave Technology,2016,34(2):525-543.
[2]CHEN M,YOU S.Analysis of OSNR monitoring in optical fiber communication system[C]//2014 13th International Conference on Optical Communications and Networks(ICOCN).2014:1-4.
[3]LUNDBERG L,SUNNERUD H,JOHANNISSON P.In-BandOSNR Monitoring of PM-QPSK Using the Stokes Parameters[C]//Optical Fiber Communications Conference and Exhibition (OFC).2015:1-3.
[4]DO C,TRAN A V,ZHU C,et al.Data-Aided OSNR Estimation for QPSK and 16-QAM Coherent Optical System[J].IEEE Photonics Journal,2013,5(5):6601609.
[5]YE H,JIANG H,LIANG G,et al.OSNR monitoring based on a low-bandwidth coherent receiver and LSTM classifier[J].Optics.Express,2021,29(2):1566-1577.
[6]ZHU X,LIU B,REN J,et al.Optical performance monitoringusing lifelong learning with confrontational knowledge distillation in 7-core fiber for elastic optical networksp[J].Optics Express,2022,30(15):27109-27122.
[7]HUANG Z,TIAN Y,LIU Y F,et al.Optical performance monitoring using SOI-based spectral analysis[J].Optics Express,2022,30(4):6397-6412.
[8]CHAI J,ZHAO Y,CHEN X,et al.Cost-effective OSNR monitoring with large chromatic dispersion tolerance using random forest for intermediate nodes[J].Optics Communications,2021,479(13):126469.
[9]HU C J,ZHENG H,LI W,et al.Modulation-format-indepen-dent in-band OSNR monitoring technique using Gaussian process regression for a Raman amplified multi-span system with a cascaded filtering effect[J].Optics Express,2020,28(7):10134-10144.
[10]GUO Z,LIU B,REN J X,et al.Modulation format recognition with transfer learning assisted convolutional neural network using multiple Stokes sectional plane image in multi-core fibers[J].Optics.Express 2022,30(12):21990-22005.
[11]JOVANOVIC O,YANKOV M P,ROS F D,et al.End-to-End Learning of a Constellation Shape Robust to Channel Condition Uncertainties[J].Lightwave Technology,2022,40(12):3316-3324.
[12]ZHU X,LIU B,ZHU X,et al.Multiple Stokes sectional plane image based modulation format recognition with a generative adversarial network[J].Optics Express,2021,29(20):31836-31848.
[13]ZHONG Q,LIU B,REN J,et al.Self-propagated chaotic dyna-mically enhanced optical physical layer encryption communication system based on bidirectional long short-term memory neural network[J].Optics Express,2022,30(20):36379-36393.
[14]GAO J,ZHOU X,FAN Q,et al.Modulation format and baud-rate identification using asynchronous single channel sampling based on CNN[J].Optics Communications,2020,463(12):125363.
[15]HYUNG J C,DANIEL L,VARGHESE A,et al.Constellation-based identification of linear and nonlinear OSNR using machine learning:a study of link-agnostic performance[J].Optics Express,2022,30:2693-2710.
[16]WAN Z Q,YU Z M,LIANG S,et al.Intelligent Optical Per-formance Monitor Using Multi-task Learning Based Artificial Neural Network[J].Optics Express,2019,27(8):4018-4027.
[17]XIANG Q,YANG Y,ZHANG Q,et al.Joint and Accurate OSNR Estimation and Modulation Format Identification Scheme Using the Feature-Based ANN[J].IEEE Photonics Journal,2019,11(4):1-11.
[18]WANG D,ZHANG M,LI J,et al.Intelligent constellation diagram analyzer using convolutional neural network-based deep learning[J].Optics Express,2017,25(15):17150.
[19]YU C L,WANG H Y,KE C J,et al.Multi-Task Learning Con-volutional Neural Network and Optical Spectrums Enabled Optical Performance Monitoring[J].IEEE Photonics Journal,2022,14(2):1-8.
[20]LIN T,ROYCHOWDHURY A,MAJI S.Bilinear CNN Models for Fine-Grained Visual Recognition[C]//IEEE International Conference on Computer Vision(ICCV).2015:1449-1457.
[21]YU C,ZHAO X,ZHENG Q,et al.Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition[C]//European Conference on Computer Vision(ECCV).2018:595-610.
[22]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.

相关文章 15

[1]	彭钺峰, 赵波, 刘会, 安杨. 针对机器学习的成员推断攻击综述 Survey on Membership Inference Attacks Against Machine Learning 计算机科学, 2023, 50(3): 351-359. https://doi.org/10.11896/jsjkx.220100016
[2]	徐夏, 张晖, 杨春明, 李波, 赵旭剑. 公平谱聚类方法用于提高簇的公平性 Fair Method for Spectral Clustering to Improve Intra-cluster Fairness 计算机科学, 2023, 50(2): 158-165. https://doi.org/10.11896/jsjkx.211100279
[3]	王艺潭, 王一舒, 袁野. 学习索引研究综述 Survey of Learned Index 计算机科学, 2023, 50(1): 1-8. https://doi.org/10.11896/jsjkx.211000149
[4]	陈得鹏, 刘肖, 崔杰, 何道敬. 面向机器学习的成员推理攻击综述 Survey of Membership Inference Attacks for Machine Learning 计算机科学, 2023, 50(1): 302-317. https://doi.org/10.11896/jsjkx.220800227
[5]	冷典典, 杜鹏, 陈建廷, 向阳. 面向自动化集装箱码头的AGV行驶时间估计 Automated Container Terminal Oriented Travel Time Estimation of AGV 计算机科学, 2022, 49(9): 208-214. https://doi.org/10.11896/jsjkx.210700028
[6]	宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[7]	何强, 尹震宇, 黄敏, 王兴伟, 王源田, 崔硕, 赵勇. 基于大数据的进化网络影响力分析研究综述 Survey of Influence Analysis of Evolutionary Network Based on Big Data 计算机科学, 2022, 49(8): 1-11. https://doi.org/10.11896/jsjkx.210700240
[8]	李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩. 基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究 Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network 计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094
[9]	张光华, 高天娇, 陈振国, 于乃文. 基于N-Gram静态分析技术的恶意软件分类研究 Study on Malware Classification Based on N-Gram Static Analysis Technology 计算机科学, 2022, 49(8): 336-343. https://doi.org/10.11896/jsjkx.210900203
[10]	陈明鑫, 张钧波, 李天瑞. 联邦学习攻防研究综述 Survey on Attacks and Defenses in Federated Learning 计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079
[11]	李亚茹, 张宇来, 王佳晨. 面向超参数估计的贝叶斯优化方法综述 Survey on Bayesian Optimization Methods for Hyper-parameter Tuning 计算机科学, 2022, 49(6A): 86-92. https://doi.org/10.11896/jsjkx.210300208
[12]	赵璐, 袁立明, 郝琨. 多示例学习算法综述 Review of Multi-instance Learning Algorithms 计算机科学, 2022, 49(6A): 93-99. https://doi.org/10.11896/jsjkx.210500047
[13]	肖治鸿, 韩晔彤, 邹永攀. 基于多源数据和逻辑推理的行为识别技术研究 Study on Activity Recognition Based on Multi-source Data and Logical Reasoning 计算机科学, 2022, 49(6A): 397-406. https://doi.org/10.11896/jsjkx.210300270
[14]	姚烨, 朱怡安, 钱亮, 贾耀, 张黎翔, 刘瑞亮. 一种基于异质模型融合的 Android 终端恶意软件检测方法 Android Malware Detection Method Based on Heterogeneous Model Fusion 计算机科学, 2022, 49(6A): 508-515. https://doi.org/10.11896/jsjkx.210700103
[15]	王飞, 黄涛, 杨晔. 基于Stacking多模型融合的IGBT器件寿命的机器学习预测算法研究 Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion 计算机科学, 2022, 49(6A): 784-789. https://doi.org/10.11896/jsjkx.210400030

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed