Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks

Abstract

We introduce a ML-based architecture for network operators to detect impairments from specific OSaaS users while blind to the users' internal spectrum details. Experimental studies with three OSaaS users demonstrate the model's capability to accurately classify the source of impairments, achieving classification accuracy of 94.2%.

This paper is a preprint of a paper accepted to ECOC 2024 and is subject to Institution of Engineering and Technology Copyright. A copy of record will be available at IET Digital Library