The reduction of system margin in open optical line systems (OLSs) requires the capability to predict the quality of transmission (QoT) within them. This quantity is given by the generalized signal-to-noise ratio (GSNR), including both the effects of amplified spontaneous emission (ASE) noise and nonlinear interference accumulation. Among these, estimating the ASE noise is the most challenging task due to the spectrally resolved working point of the erbium-doped fiber amplifiers (EDFAs), which depend on the spectral load, given the overall gain profile. An accurate GSNR estimation enables control of the power optimization and the possibility to automatically deploy lightpaths with a minimum margin in a reliable manner. We suppose an agnostic operation of the OLS, meaning that the EDFAs are operated as black boxes and rely only on telemetry data from the optical channel monitor at the end of the OLS. We acquire an experimental data set from an OLS made of 11 EDFAs and show that, without any knowledge of the system characteristics, an average extra margin of 2.28 dB is necessary to maintain a conservative threshold of QoT. Following this, we applied deep neural network machine-learning techniques, demonstrating a reduction in the needed margin average down to 0.15 dB.

中文翻译：

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在开放式光缆系统控制器中使用机器学习

开放式光线路系统（OLS）中系统余量的减少要求能够预测其中的传输质量（QoT）。该量由广义信噪比（GSNR）给出，包括放大的自发发射（ASE）噪声和非线性干扰累积的影响。其中，由于掺-光纤放大器（EDFA）的频谱分辨工作点取决于总的频谱负载，因此估计ASE噪声是最具挑战性的任务。准确的GSNR估算可以控制功率优化，并可以以可靠的方式以最小的余量自动部署光路。我们假设OLS是不可知的操作，这意味着EDFA被当作黑匣子操作，并且仅依赖于OLS末尾的光学通道监视器的遥测数据。我们从由11个EDFA制成的OLS中获得了一个实验数据集，结果表明，在不了解系统特性的情况下，平均需要2.28 dB的额外余量才能保持QoT的保守阈值。此后，我们应用了深度神经网络机器学习技术，证明所需的余量平均值降低到0.15 dB。