Soft-Failure Detection, Localization, Identification, and Severity Prediction by Estimating QoT Model Input Parameters

The performance of optical devices can degrade because aging and external causes like, for example, temperature variations. Such degradation might start with a low impact on the Quality Transmission (QoT) supported lightpaths (soft-failure). However, it degenerate into hard-failure if device itself is not repaired or replaced, an cause responsible properly addressed. In this work, we propose comparing QoT measured in transponders one estimated using tool. Those deviations be explained by changes value input parameters model representing devices, like noise figure amplifiers reduced Optical Signal to Noise Ratio Wavelength Selective Switches. By applying reverse engineering, those modeling as function observed lightpaths. Experiments reveal high accuracy estimation parameters, results obtained simulation show large anticipation soft-failure detection localization, well accurate identification degradations before they have major network.