Assessment on the in-field lightpath QoT computation including connector loss uncertainties

Reliable and conservative computation of the quality of transmission (QoT) of transparent lightpaths (LPs) is a crucial need for software-defined control and management of the wavelength division multiplexing optical transport. The LP QoT is summarized by the generalized SNR (GSNR) that can be computed by a QoT estimator (QoT-E). Within the context of network automation, the QoT-E must rely only on data from the network controller or provided by network elements through common control protocols and data structures. Therefore, given the theoretical accuracy of the QoT-E, the in-field accuracy in the GSNR computation is also determined by the level of knowledge of input parameters. Among these, a fundamental value is the connector loss at the input of each fiber span, which defines the actual power levels triggering the nonlinear effects in the fiber, and so defining the amount of nonlinear interference and spectra tilt due to the stimulated Raman scattering introduced by the fiber span. This value cannot be easily measured and may vary in time because of equipment update or maintenance. In this paper, we consider a lab measurement campaign in which the GSNR has been computed by means of the open source project Gaussian noise model in Python (GNPy) and analyze the computation error distribution. We show how the assumption on the value for the connector loss modifies the GSNR computation error and how the GSNR computation is more conservative while accurate at the lower values for the connector loss. Using the outcome of the measurement campaign carried out in the laboratory, we present results on the error of GSNR computation in a production network, specifically, over two paths of the Microsoft core network. Using GNPy with the assumption of a connector loss of 0.25 dB as derived from the measurement campaign carried out in the laboratory, and using the physical layer description from the network controller, we show that GNPy is not conservative by overestimating the GSNR in only 5% of cases, while in conservative predictions, the underestimation error exceeds 1 dB only for a few outliers.