Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning

Xiaochuan SUN; Zhigang LI; Minghui ZHANG; Guan GUI

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

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Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning

Topics: Communication and Computing Fusion of Intelligent Emergency | 更新时间：2024-06-05

- Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning
- Journal on Communications Vol. 41, Issue 9, Pages: 1-7(2020)
- 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：
- 基金信息：
  
  The Natural Science Foundation of Hebei Province(F201820918);The Hebei Colleges and Universities Science Foundation(QN2018115);The S＆T Major Project of the Science and Technology Ministry of China(2017YFE0135700);The Office of Science and Technology of Tangshan(19150230E)
- DOI：10.11959/j.issn.1000-436x.2020201
  CLC： TN91
- Online First：2020-09，
  
  Published：25 September 2020
- 稿件说明：
移动端阅览
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：

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

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