Increasing traffic demands are causing network operators to adopt disaggregated and open networking solutions to better exploit optical transmission capacity, and consequently enable a software-defined networking (SDN) approach to control and management that encompasses the WDM data transport layer. In these frameworks, a quality of transmission estimator (QoT-E) that gives the generalized signal-to-noise ratio (GSNR) is commonly used to compute the feasibility of transparent lightpaths (LP)s, taking into account the amplified spontaneous emission (ASE) noise and the nonlinear interference (NLI). In general, the ASE noise is the main contributor to the GSNR and is also the most challenging noise component to evaluate in a scenario with varying spectral loads, due to fluctuations in the optical amplifier responses. In this work, we propose a machine learning (ML) algorithm that is trained using different ASE-shaped spectral loads in order to predict the OSNR component of the GSNR; this methodology is subsequently used in combination with a QoT-E in the lightpath computation engine (L-PCE). We present an experiment on a point-to-point optical line system (OLS), including 9 commercial erbium-doped fiber amplifiers (EDFA)s used as black-boxes, each with variable gain and tilt values, and 8 fibers that are characterized by distinct physical parameters. Within this experiment, we receive the signal at the end of the OLS, measuring the bit-error-rate (BER) and the power spectrum, over 2520 different spectral loads. From this dataset, we extract the expected GSNRs and their linear and nonlinear components. Through joint application of a ML algorithm and the open-source GNPy library, we obtain a complete QoT-E, demonstrating that a reliable and accurate LP feasibility predictor may be implemented.

中文翻译：

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通过部分分解光网络中的机器学习增强光路 QoT 计算


不断增长的流量需求促使网络运营商采用分解和开放的网络解决方案，以更好地利用光传输容量，从而启用包含 WDM 数据传输层的软件定义网络 (SDN) 方法来进行控制和管理。在这些框架中，给出广义信噪比（GSNR）的传输质量估计器（QoT-E）通常用于计算透明光路（LP）的可行性，同时考虑放大的自发发射（ ASE）噪声和非线性干扰（NLI）。一般来说，ASE 噪声是 GSNR 的主要贡献者，而且由于光放大器响应的波动，它也是在光谱负载变化的场景中评估时最具挑战性的噪声分量。在这项工作中，我们提出了一种机器学习（ML）算法，该算法使用不同的 ASE 形频谱负载进行训练，以预测 GSNR 的 OSNR 分量；该方法随后与光路计算引擎 (L-PCE) 中的 QoT-E 结合使用。我们提出了一个关于点对点光线路系统 (OLS) 的实验，包括 9 个用作黑匣子的商用掺铒光纤放大器 (EDFA)，每个放大器都具有可变增益和倾斜值，以及 8 根经过表征的光纤通过不同的物理参数。在此实验中，我们在 OLS 末尾接收信号，测量误码率 (BER) 和功率谱，超过 2520 个不同的频谱负载。从这个数据集中，我们提取了预期的 GSNR 及其线性和非线性分量。 通过ML算法和开源GNPy库的联合应用，我们获得了完整的QoT-E，证明可以实现可靠且准确的LP可行性预测器。