Artificial Neural Networks to estimate Blocking Probability of transparent optical networks: A robustness study for different networks

Recent studies demonstrated the advantage of alternative methods to assess optical networks based on Artificial Neural Networks (ANN), which is to obtain a fast estimation of Blocking Probability (BP) with a small error. In previous works we proposed the use of ANNs to predict the BP of optical networks with dynamic traffic by using topological metrics and general information of the physical layer. In this paper we use the node locations of six deployed networks in order to evaluate the robustness of the estimator. We also propose four new measures related to physical layer and we compare the results of our proposal with the outcome of a discrete event network simulator. From our results we conclude that ANN is a promising technique to estimate the BP of transparent optical networks because we obtained a fast BP estimation with small errors for all analyzed networks.