Machine learning (ML) is increasingly applied in optical network management, especially in cross-layer frameworks where physical layer characteristics may trigger changes at the network layer due to transmission performance measurements (quality of transmission, QoT) monitored by optical equipment. Leveraging ML-based QoT estimation approaches has proven to be a promising alternative to exploiting classical mathematical methods or transmission simulation tools. However, supervised ML models rely on large representative training sets, which are often unavailable, due to the lack of the necessary telemetry equipment or of historical data. In such cases, it can be useful to use training data collected from a different network. Unfortunately, the resulting models may be uneffective when applied to the current network, if the training data (the source domain) is not well representative of the network under study (the target domain). Domain adaptation (DA) techniques aim at tackling this issue, to make possible the transfer of knowledge among different networks. This paper compares several DA approaches applied to the problem of estimating the QoT of an optical lightpath using a supervised ML approach. Results show that, when the number of samples from the target domain is limited to a few dozen, DA approaches consistently outperform standard supervised ML techniques.

中文翻译：

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域自适应技术对光网络中传输质量评估的好处

机器学习（ML）越来越多地应用于光网络管理中，尤其是在跨层框架中，由于光学设备监视的传输性能测量（传输质量，QoT），物理层特性可能会触发网络层的变化。利用基于ML的QoT估计方法已被证明是开发经典数学方法或传输仿真工具的有前途的替代方法。但是，由于缺少必要的遥测设备或历史数据，有监督的ML模型依赖于大型的代表性训练集，而这些训练集通常不可用。在这种情况下，使用从其他网络收集的训练数据可能会很有用。不幸的是，当应用于当前网络时，所得模型可能无效，如果训练数据（源域）不能很好地代表所研究网络（目标域）。域适应（DA）技术旨在解决此问题，以使不同网络之间的知识转移成为可能。本文比较了几种采用监督ML方法应用于估计光路QoT问题的DA方法。结果表明，当来自目标域的样本数量限制为几十个时，DA方法始终优于标准监督的ML技术。本文比较了几种采用监督ML方法应用于估计光路QoT问题的DA方法。结果表明，当来自目标域的样本数量限制为几十个时，DA方法始终优于标准监督的ML技术。本文比较了几种采用监督ML方法应用于估计光路QoT问题的DA方法。结果表明，当来自目标域的样本数量限制为几十个时，DA方法始终优于标准监督的ML技术。