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.

中文翻译：

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评估包括连接器损耗不确定性在内的现场光路QoT计算

透明光路（LP）传输质量（QoT）的可靠和保守计算是对波分复用光传输的软件定义控制和管理的关键需求。LP QoT由可以由QoT估计器（QoT-E）计算的广义SNR（GSNR）概括。在网络自动化的范围内，QoT-E必须仅依靠来自网络控制器的数据或由网络元素通过通用控制协议和数据结构提供的数据。因此，给定QoT-E的理论精度，GSNR计算中的场内精度也取决于输入参数的知识水平。其中一个基本值是每个光纤跨段输入处的连接器损耗，它定义了触发光纤中非线性效应的实际功率电平，并因此定义了由于光纤跨度引入的受激拉曼散射而引起的非线性干扰量和频谱倾斜。由于设备更新或维护，该值不易测量，并且可能随时间变化。在本文中，我们考虑了一个实验室测量活动，在该活动中，已通过Python的开源项目高斯噪声模型（GNPy）计算了GSNR，并分析了计算误差分布。我们将显示对连接器损耗值的假设如何修改GSNR计算误差，以及如何在较低连接器损耗值的情况下使GSNR计算更加保守而准确。利用实验室进行的测量活动的结果，我们提出了有关生产网络中GSNR计算错误的结果，特别是在Microsoft核心网络的两条路径上。使用GNPy并假设从实验室进行的测量活动得出的连接器损耗为0.25 dB，并使用网络控制器的物理层描述，我们通过仅高估5％的GSNR来显示GNPy并不保守在保守的预测中，仅对于少数异常值，低估误差超过1 dB。