Topological Design Aspects of Super C+L-Band Optical Backbone Networks Using Machine Learning

A promising approach to alleviate the emerging capacity limitations in backbone optical networks is to employ the Super C+L-band, which provides an available spectrum of roughly 12 THz. Network throughput is a key metric for analyzing the performance of such networks; however, evaluating this metric is a complex task due to the interplay between physical and network layer aspects. Physical modeling, which accounts for signal impairments, is particularly complex in these scenarios due to the presence of Stimulated Raman Scattering (SRS), which transfers energy from the C to the L band. On the other hand, network layer modeling is also challenging due to the influence of numerous factors, including physical topology, routing, and traffic characteristics. For the networks considered here, we propose a machine learning approach to predict both the network throughput and the average channel capacity for the Shannon and real cases, and to investigate how these metrics depend on various physical topology parameters. The approach relies on an Artificial Neural Network (ANN) model, whose predictions are interpreted using the SHapley Additive exPlanations (SHAP) method to identify the importance of various topological parameters. Furthermore, the ANN is trained using data obtained from a previously developed simulator that takes into account both physical and network aspects. The analysis provides valuable insights for designing future ultra-high-capacity optical backbone networks.