基于集成学习的广域光骨干网多信道传输质量预测方法

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

Abstract

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.

Key words: optical backbone network, multi-channel, QoT prediction, ensemble learning

CLC Number:

TN91

Xiaochuan SUN,Zhigang LI,Minghui ZHANG,Guan GUI. Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning[J]. Journal on Communications, 2020, 41(9): 1-7.

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结构	迭代次数/次	学习速率	验证方法
MLP₁	101	5×10^-4	五折交叉验证
MLP₂	151	2×10^-4	五折交叉验证
MLP₃	151	4×10^-4	五折交叉验证
MLP₄	201	3×10^-4	五折交叉验证
MLP₅	101	4×10^-4	五折交叉验证
MLP₆	1	1×10^-4	五折交叉验证

模型	MAE	MSE
ANN	0.023 4	1.238 9×10^-3
EL-MLP	0.004 1	2.630 0×10^-5

模型	标准	场景1	场景2	场景3	场景4
ANN	MAE	0.043 7	0.029 8	0.040 1	0.047 0
EL-MLP	MAE	0.005 5	0.004 4	0.005 1	0.004 7
ANN	MSE (×10^-3)	3.192 0	1.558 1	2.970 3	3.682 2
EL-MLP	MSE (×10^-5)	5.196 2	3.505 7	4.888 3	4.765 1

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

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