Estimating Quality of Transmission in a Live Production Network using Machine Learning

TL;DR

This paper presents a machine learning approach using neural networks trained on synthetic data to accurately estimate the quality of transmission in a live network, achieving high precision across various configurations.

Contribution

It introduces a novel ML-based method for QoT estimation in live networks using synthetic training data, enhancing prediction accuracy and operational efficiency.

Findings

Predicts lightpath performance with <0.5dB SNR error

Utilizes synthetic data for training neural networks

Effective across a wide range of network configurations

Abstract

We demonstrate QoT estimation in a live network utilizing neural networks trained on synthetic data spanning a large parameter space. The ML-model predicts the measured lightpath performance with <0.5dB SNR error over a wide configuration range.