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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用于自主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，首选闭环自治架构。考虑到光网络中的大量光器件和每个光收发器中的许多数字信号处理模块，可以收集海量的实时数据。然而，对于传统的监控结构，收集、存储和处理大量数据是具有挑战性的任务。此外，没有适当考虑来自不同来源和地区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，本文提出了物理层和控制层之间的数据融合辅助遥测层。数据融合方法在三个不同的层次上进行了阐述：源级 , 空间层次 和 模型层 . 对于每个级别，介绍了各种数据融合算法并回顾了相关工作。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。