A Data-Fusion-Assisted Telemetry Layer for Autonomous Optical Networks

TL;DR

This paper proposes a data-fusion-assisted telemetry layer for autonomous optical networks, enhancing capacity and reliability by effectively integrating multi-source data at various levels for improved monitoring and control.

Contribution

It introduces a novel telemetry layer with multi-level data fusion methodologies, addressing data processing challenges and improving network monitoring accuracy.

Findings

Simulations demonstrate improved data integration and network monitoring.

The proposed layer enhances capacity and reliability of optical networks.

Various data fusion algorithms are effectively applied at different levels.

Abstract

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.