Loading [a11y]/accessibility-menu.js
On the Application of Explainable Artificial Intelligence to Lightpath QoT Estimation | IEEE Conference Publication | IEEE Xplore

On the Application of Explainable Artificial Intelligence to Lightpath QoT Estimation


Abstract:

We demonstrate the potentialities of explainable AI when applied to distill knowledge from a trained supervised machine learning model for lightpath quality of transmissi...Show More

Abstract:

We demonstrate the potentialities of explainable AI when applied to distill knowledge from a trained supervised machine learning model for lightpath quality of transmission estimation in optical networks, with synthetic datasets. © 2021 The Author(s)
Date of Conference: 06-10 March 2022
Date Added to IEEE Xplore: 13 April 2022
ISBN Information:
Conference Location: San Diego, CA, USA

1. Introduction

In the last few years, the application of Machine Learning (ML) technologies has been widely investigated in several fields of optical communications and networking, including e.g. lightpath Quality of Transmission (QoT) estimation, automated fault management, routing and wavelength/spectrum assignment [1]. However, the vast majority of ML approaches proposed in the scientific literature are used as “black boxes”, i.e., they do not expose their internal mechanics nor the decisional processes adopted to associate the produced outputs with the set of feature values provided as input. This hinders the interpretability of the models and prevents the extraction of useful insights that could be leveraged to better understand the nature of the problem at hand.

Contact IEEE to Subscribe

References

References is not available for this document.