Conceptualizing an Effective Machine Learning Evaluation Framework for Optical Networks Design

At the present day, the evaluation of machine learning (ML) as a candidate for substituting analytical quality of transmission (QoT) estimators is done in a compartmentalized way. The assessment is not produced from a global optical network design perspective and with accurate optical design metrics. In this paper, we determine a suitable methodology for evaluating the QoT substitution based on the foundational idea that different QoT estimators should be compared by the equity of their overestimation likelihood, which drives system margins. To demonstrate the potential drawbacks of a non-adequate assessment of the QoT substitution, we use the proposed method in several scenarios, proving that we can achieve gains in QoT estimation error or design margins while observing notable losses in terms of network throughput