Experimental Validation of Machine Learning-Based Joint Failure Management and Quality of Transmission Estimation

Abstract

The exponentially growing demand for high-speed data necessitates more complex and versatile networks. Optimization and reliability assurance of such high-complexity networks is getting increasingly important. In this paper, we experimentally validate our a machine learning-based framework that combines quality of transmission (QoT) estimation with soft-failure detection, identification, and localization based on the same latent space of a variational autoencoder running on optical spectra obtained by optical spectrum analyzers at high priority nodes in the network. We further investigate the advantages of a variational autoencoder-based soft-failure detection mechanism over a QoT metric-based approach. We use data acquired from optical transmission experiments involving different modulation formats and channel configurations. The results demonstrate that the proposed framework achieves reliable QoT estimation in real world scenarios. Additionally, it effectively detects soft-failures, identifies specific failure types and accurately localizes the occurrence of failures.