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2021
Conference Paper
Titel
Deep Convolutional Neural Network for Network-wide QoT Estimation
Abstract
We propose a novel Deep Convolutional Neural Network formulation for network-wide QoT classification tasks and show its effectiveness for networks with significant topological differences. Our formulation achieves 99% accuracy on large and diverse test datasets.