基于机器学习的光链路建立中的传输质量预测技术

Transmission quality prediction technology in optical link building based on machine learning

传输质量(QoT)预测在光网络中日趋重要,机器学习成为今后实现光网络中QoT预测的重要手段。提出一种基于机器学习分类器的QoT预测技术。通过传输方程生成所需的数据,用于之后的分类器训练和性能测试,并仿真验证了K最近邻(KNN)、逻辑回归(LR)和支持向量机(SVM)这3种常用的分类器的性能。仿真结果表明:相较于传统的QoT估计方法,基于机器学习的方法在有效地降低计算复杂度的前提下,还能提供相当高的预测精度,是一种具有广阔应用前景的QoT估计新方案。

The prediction of transmission quality(QoT)is becoming more and more important in optical networks.Machine learning has become an important means to realize the prediction of QoT in optical networks in the future.This paper presents a new technology of QoT prediction based on machine learning classifier.Through the data generated by the transfer equation,it can be used for classifier training and performance testing.The performance of K-Nearest Neighbor(KNN),logistic regression(LR)and support vector machine(SVM)are verified by simulation.The simulation results show that,compared with the traditional QoT estimation method,the machine learning based on method can effectively reduce the computational complexity and provide quite high prediction accuracy.This method is a new QoT estimation scheme with broad application prospects.