Impact of Machine Learning Algorithms on WDM High-Speed Optical Networks

Abstract

This paper focuses on comparing the various machine learning (ML) algorithms that can be applicable in wavelength division multiplexing (WDM) optical networks to provide better simulation outcomes. ML, combined with WDM optical networks, helps in network control and resource management that are useful in service provisioning and resource assignment. This paper gives a comprehensive review of machine learning approaches in WDM optical networks concerning support vector machine (SVM), K-nearest neighbour (K-NN), decision tree, random forest and neural networks algorithms. These algorithms’ performances are compared in terms of accuracy and AUC; further, the accuracy and AUC results show an average outcome of 99% and 0.98, respectively. Simulation can be performed on MATLAB and Net2plan tools using different data sets in terms of average accuracy and AUC for WDM optical networks. This research’s future directions can be towards ML utilization to provide optimal routing and wavelength assignment, increasing bandwidth utilization to reduce control overheads, reduce computational complexity, security, fault occurrence and monitoring schemes for WDM optical networks supporting 5G applications.