Improved QoT estimations through refined signal power measurements and data-driven parameter optimizations in a disaggregated and partially loaded live production network

Accurate quality of transmission (QoT) estimations are essential enablers for future low-margin dynamic optical network operations. However, physical parameter measurement uncertainties and other intractable signal propagation effects degrade the accuracy of QoT estimation, especially in live production networks. The recent trend of network disaggregation further exacerbates the issue, and a vendor-agnostic accurate QoT estimator is much needed. In this paper, we study Gaussian-noise-model-based QoT estimation in a large-scale disaggregated and partially loaded live production network with monitored physical layer data spanning across 8 months. We first propose refining the signal power measurements by combining the inline amplifier and optical channel monitor (OCM) power measurements, followed by estimating the gain and noise power profiles of each inline amplifier, which in turn improves QoT estimation accuracy. We further introduce an optical multiplex section and frequency bias to the analytical model to incorporate intractable location-specific and spectral effects in the network and proposed data-driven parameter optimizations to learn the biases as well as erbium-doped fiber amplifier noise figures. The (mean, standard deviation) of the QoT estimation errors were reduced from (-0.1043, 0.6037) dB using average amplifier power and (-0.7875, 0.6337) dB using OCM power to (-0.0964, 0.4649) dB after input parameter refinements and were further reduced to (0.0046, 0.2377) dB with data-driven parameter optimization. The proposed methodologies are simple procedures that network operators can adopt to optimize analytical-model-based QoT estimators and/or serve as feature engineering procedures preceding machine-learning-based QoT in realistic disaggregated live production networks.