  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.
