Machine-learning-based impairment-aware dynamic RMSCA in multi-core elastic optical networks

Abstract

This paper presents a routing, modulation, spectrum, and core assignment (RMSCA) algorithm for space-division-multiplexing-based elastic optical networks (SDM-EONs) comprising multi-core links. A network state-dependent route and core selection method is proposed using a deep neural network (DNN) classifier. The DNN is trained using a metaheuristic optimization algorithm to predict lightpath suitability, considering the quality of transmission and resource availability. Physical layer impairments, including inter-core crosstalk, amplified spontaneous emission, and Kerr fiber nonlinearities, are considered, and a random forest (RF)-based link noise estimator is proposed. A feature importance selection analysis is provided for all the features considered for the DNN classifier and the RF link noise estimator. The proposed machine-learning-enabled RMSCA approach is evaluated on three network topologies, USNET, NSFNET, and COST-239 with 7-core and 12-core fiber links. It is shown to be superior in terms of blocking probability, bandwidth blocking probability, and acceptable computational speed compared to the standard and published benchmarks at different traffic loads.