基于集成学习的广域光骨干网多信道传输质量预测方法

孙晓川; 李志刚; 张明辉; 桂冠

doi:10.11959/j.issn.1000-436x.2020201

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基于集成学习的广域光骨干网多信道传输质量预测方法

专题：面向智慧应急的通信与计算融合 | 更新时间：2024-06-05

- 基于集成学习的广域光骨干网多信道传输质量预测方法
- Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning
- 通信学报 2020年41卷第9期页码：1-7
- 作者机构：
  
  1. 南京邮电大学通信与信息工程学院，江苏南京 210003
  2. 华北理工大学人工智能学院，河北唐山 063210
  3. 河北工业大学电子信息工程学院，天津 300401
- 作者简介：
  
  [ "孙晓川（1983- ），男，山东烟台人，博士，华北理工大学副教授，主要研究方向为未来通信网络关键技术、机器学习、群体智能" ]
  [ "李志刚（1966- ），男，河北唐山人，博士，华北理工大学副教授，主要研究方向为网络控制理论、深度学习、数据挖掘技术" ]
  [ "张明辉（1994- ），女，河北承德人，河北工业大学博士生，主要研究方向为机器学习、计算智能与无线网络" ]
  [ "桂冠（1982- ），男，安徽枞阳人，博士，南京邮电大学教授，主要研究方向为基于深度学习的物理层无线通信技术" ]
- 基金信息：
  
  河北省自然科学基金资助项目(F201820918);河北省高等学校科学技术研究基金资助项目(QN2018115);国家科技部科技重大基金资助项目(2017YFE0135700);唐山市科技计划基金资助项目(19150230E)
- DOI：10.11959/j.issn.1000-436x.2020201
  中图分类号： TN91
- 网络出版日期：2020-09，
  
  纸质出版日期：2020-09-25
- 稿件说明：
移动端阅览
孙晓川, 李志刚, 张明辉, 等. 基于集成学习的广域光骨干网多信道传输质量预测方法[J]. 通信学报, 2020,41(9):1-7.

Xiaochuan SUN, Zhigang LI, Minghui ZHANG, et al. Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning[J]. Journal on communications, 2020, 41(9): 1-7.
孙晓川, 李志刚, 张明辉, 等. 基于集成学习的广域光骨干网多信道传输质量预测方法[J]. 通信学报, 2020,41(9):1-7. DOI： 10.11959/j.issn.1000-436x.2020201.

Xiaochuan SUN, Zhigang LI, Minghui ZHANG, et al. Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning[J]. Journal on communications, 2020, 41(9): 1-7. DOI： 10.11959/j.issn.1000-436x.2020201.

摘要

针对动态广域光骨干网中光信道传输质量预测方法精确度不足的问题，以集成学习理论为基础提出一种光信道传输质量预测方法。首先，在堆栈集成学习框架下构建了由5个多层感知器模型组成的基学习器，通过并行组合的方式实现了样本数据的同态集成学习。然后，融合基学习器的预测结果形成新的训练集，用于训练由单一多层感知器组成的元学习器。仿真结果表明，对比深度神经网络，所提方法在单信道和多信道QoT预测场景下具有更优秀的非线性逼近性能，预测精度分别提高了1.93%和3.82%。

Abstract

Due to the fact that in dynamic wide-area optical backbone network the accuracies of the existing prediction methods were insufficient

a novel prediction method on quality of transmission (QoT) of optical channel was proposed based on ensemble learning theory.Firstly

under the framework of stacked ensemble learning

a base-learner including five multilayer perceptron (MLP) model was built

which could achieve homomorphic ensemble learning of sample data through parallel combination.Subsequently

the new training set fused from the predicted results of the preceding base-learner was used to training the meta-learner composed of a single MLP.The simulation results show that compared with the used deep neural network

the proposed method can obtain a more excellent nonlinear approximation in the scenarios of the single-channel and multi-channels

and the prediction accuracies have the improvements of 1.93% and 3.82% respectively.

关键词

Keywords

references

张平 , 陶运铮 , 张治 . 5G 若干关键技术评述 [J ] . 通信学报 , 2016 , 37 ( 7 ): 15 - 29 .

ZHANG P , TAO Y Z , ZHANG Z . Survey of several key technologies for 5G [J ] . Journal on Communications , 2016 , 37 ( 7 ): 15 - 29 .

尤肖虎 , 潘志文 , 高西奇 , 等 . 5G移动通信发展趋势与若干关键技术 [J ] . 中国科学:信息科学 , 2014 , 44 ( 5 ): 551 - 563 .

YOU X H , PAN Z W , GAO X Q , et al . The 5G mobile communication:the development trends and its emerging key techniques [J ] . SCIENTIA SINICA Informationis , 2014 , 44 ( 5 ): 551 - 563 .

赵国锋 , 陈婧 , 韩远兵 , 等 . 5G移动通信网络关键技术综述 [J ] . 重庆邮电大学学报(自然科学版) , 2015 , 27 ( 4 ): 441 - 452 .

ZHAO G F , CHEN J , HAN Y B , et al . Prospective network techniques for 5G mobile communication:a survey [J ] . Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) , 2015 , 27 ( 4 ): 441 - 452 .

KHODASHENAS P S , AZNAR J , LEGARREA A , et al . 5G network challenges and realization insights [C ] // 2016 18th International Conference on Transparent Optical Networks (ICTON) . Piscataway:IEEE Press , 2016 : 1 - 4 .

华楠 , 郑小平 . 智能光网络发展历程的回顾和展望:从ASON到PCE,再到SDON [J ] . 电信科学 , 2014 , 30 ( 2 ): 88 - 98 .

HUA N , ZHENG X P . Review and outlook of the development course of intelligent optical networks:from ASON to PCE and then to SDON [J ] . Telecommunications Science , 2014 , 30 ( 2 ): 88 - 98 .

李少晖 , 沈世奎 . 光网络性能监测技术 [J ] . 电信网技术 , 2012 ( 12 ): 9 - 14 .

LI S H , SHEN S K . Optical network performance monitoring technology [J ] . Telecommunications Network Technology , 2012 ( 12 ): 9 - 14 .

张肃 , 王目光 . 基于广义回归神经网络的色散和OSNR监测 [J ] . 光电技术应用 , 2018 , 33 ( 1 ): 30 - 35 +70.

ZHANG S , WANG M G . Chromatic dispersion and OSNR monitoring based on generalized regression neural network [J ] . Electro-optic Technology Application , 2018 , 33 ( 1 ): 30 - 35 +70.

YU J , MO W , HUANG Y K , et al . Model transfer of QoT prediction in optical networks based on artificial neural networks [J ] . IEEE/OSA Journal of Optical Communications and Networking , 2019 , 11 ( 10 ): 48 - 57 .

GHOBADI M , MAHAJAN R . Optical layer failures in a large backbone [C ] // Proceedings of the 2016 Internet Measurement Conference . New York:ACM Press , 2016 : 461 - 467 .

ROTTONDI C , BARLETTA L , GIUSTI A , et al . Machine-learning method for quality of transmission prediction of unestablished lightpaths [J ] . Journal of Optical Communications and Networking , 2018 , 10 ( 2 ): 286 - 297 .

鄢然 , 郑豪 , 李蔚 . 基于机器学习的光链路建立中的传输质量预测技术 [J ] . 光通信技术 , 2020 , 44 ( 6 ): 15 - 19 .

YAN R , ZHENG H , LI W . Transmission quality prediction technology in optical link building based on machine learning [J ] . Optical Communication Technology , 2020 , 44 ( 6 ): 15 - 19 .

MORAIS R M , PEDRO J . Machine learning models for estimating quality of transmission in DWDM networks [J ] . Journal of Optical Communications and Networking , 2018 , 10 ( 10 ): 84 - 99 .

GAO Z , YAN S , ZHANG J , et al . ANN-based multi-channel QoT-prediction over a 563.4-km field-trial testbed [J ] . Journal of Lightwave Technology , 2020 , 38 ( 9 ): 2646 - 2655 .

曹莹 , 苗启广 , 刘家辰 , 等 . AdaBoost算法研究进展与展望 [J ] . 自动化学报 , 2013 , 39 ( 6 ): 745 - 758 .

CAO Y , MIAO Q G , LIU J C , et al . Advance and prospects of AdaBoost algorithm [J ] . Acta Automatica Sinica , 2013 , 39 ( 6 ): 745 - 758 .

于玲 , 吴铁军 . 集成学习:Boosting 算法综述 [J ] . 模式识别与人工智能 , 2004 , 17 ( 1 ): 52 - 59 .

YU L , WU T J . Assemble learning:a survey of boosting algorithms [J ] . Pattern Recognition and Artificial Intelligence , 2004 , 17 ( 1 ): 52 - 59 .

LARADJI I H , ALSHAVEB M , GHOUTI L . Software defect prediction using ensemble learning on selected features [J ] . Information and Software Technology , 2015 , 58 : 388 - 402 .

RIBEIRO M H D M , DOS S C L . Ensemble approach based on bagging,boosting and stacking for short-term prediction in agribusiness time series [J ] . Applied Soft Computing , 2020 86 : 105837 - 105866 .

PARK Y S , LEK S . Artificial neural networks:multilayer perceptron for ecological modeling [J ] . Developments in Environmental Modelling , 2016 , 28 : 123 - 140 .

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