Experimental Investigation of Spectral Data Enhanced QoT Estimation

Obtaining telemetry data from the optical signal without demodulation at intermediate nodes of optical networks can be achieved using optical spectrum analyzers. The information gained from the spectrum analysis can be further used for different applications, which include quality of transmission estimation (QoT). Accurate QoT estimation allows to maximize network capacity and minimize margins either through reconfiguration or during the deployment. Analytical solutions for QoT estimation require exact knowledge of the parameters (e.g. fiber lengths, attenuation, non-linearity coefficients), which are not always exactly known in practice especially in multi-vendor networks. Machine learning has shown to be able to handle such parameter-agnostic scenarios. In this paper, we experimentally compare different machine learning based QoT estimators to our developed spectral data driven estimators as well as comparing it to a new approach of using automated feature extraction from the spectrum by a variational autoencoder (VAE). The VAE-based estimation approach is experimentally validated and the required optical spectrum analyzer (OSA) resolutions are investigated. The spectral data driven estimators show to be superior regarding both R
$^{2}$
-score and mean absolute error. Furthermore, the automated feature extraction using the VAE is shown to be a suitable option for accurate optical performance monitoring without demodulation and QoT estimation.