For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an optical network and many digital signal processing modules in each optical transceiver, massive real-time data can be collected. However, for a traditional monitoring structure, collecting, storing and processing a large size of data are challenging tasks. Moreover, strong correlations and similarities between data from different sources and regions are not properly considered, which may limit function extension and accuracy improvement. To address abovementioned issues, a data-fusion-assisted telemetry layer between the physical layer and control layer is proposed in this paper. The data fusion methodologies are elaborated on three different levels: Source Level, Space Level and Model Level. For each level, various data fusion algorithms are introduced and relevant works are reviewed. In addition, proof-of-concept use cases for each level are provided through simulations, where the benefits of the data-fusion-assisted telemetry layer are shown.

中文翻译：

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自治光网络的数据融合辅助遥测层

为了进一步提高光网络的容量和可靠性，首选闭环自治体系结构。考虑到光网络中的大量光组件以及每个光收发器中的许多数字信号处理模块，可以收集大量的实时数据。但是，对于传统的监视结构，收集，存储和处理大量数据是一项艰巨的任务。此外，没有适当考虑来自不同来源和区域的数据之间的强相关性和相似性，这可能会限制功能扩展和准确性的提高。针对上述问题，本文提出了物理层与控制层之间的数据融合辅助遥测层。数据融合方法论分为三个不同层次：源层次，空间级别和模型级别。对于每个级别，都介绍了各种数据融合算法，并对相关工作进行了回顾。此外，通过模拟提供了每个级别的概念验证用例，其中显示了数据融合辅助遥测层的好处。