The demand for high-speed data is exponentially growing. To conquer this, optical networks have undergone significant changes, getting more complex and versatile. The increasing complexity necessitates that the fault management be more adaptive to enhance network assurance. In this paper, we experimentally compare the performance of soft-failure management of different machine learning algorithms. We further introduce a machine-learning-based soft-failure management framework. It utilizes a variational autoencoder-based generative adversarial network (VAE-GAN) running on optical spectral data obtained by optical spectrum analyzers. The framework is able to reliably run on a fraction of available training data as well as identify unknown failure types. The investigations show that the VAE-GAN outperforms the other machine learning algorithms when up to 10% of the total training data are available in identification tasks. Furthermore, the advanced training mechanism for the GAN shows a high F1-score for unknown spectrum identification. The failure localization comparison shows the advantage of a low complexity neural network in combination with a VAE over established machine learning algorithms.

中文翻译：

---

使用光谱进行基于机器学习的软故障管理的实验研究

对高速数据的需求呈指数级增长。为了克服这个问题，光网络发生了重大变化，变得更加复杂和多功能。日益增加的复杂性要求故障管理具有更强的适应性，以增强网络保障。在本文中，我们通过实验比较了不同机器学习算法的软故障管理性能。我们进一步介绍了基于机器学习的软故障管理框架。它利用基于变分自动编码器的生成对抗网络（VAE-GAN），在光谱分析仪获得的光谱数据上运行。该框架能够在一小部分可用训练数据上可靠地运行，并识别未知的故障类型。调查表明，当识别任务中可用总训练数据的 10% 时，VAE-GAN 的性能优于其他机器学习算法。此外，GAN 的先进训练机制在未知频谱识别方面表现出较高的 F1 分数。故障定位比较显示了低复杂度神经网络与 VAE 相结合相对于现有机器学习算法的优势。