Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning - Xiaochuan SUN; Zhigang LI; Minghui ZHANG; Guan GUI (Tongxin xuebao)
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.