Field trial of concurrent co-cable and co-trench optical fiber online identification based on ensemble learning

Abstract

The co-route optical fibers, comprising both co-cable and co-trench fibers, pose a significant potential risk to network service quality assurance by operators. They are incapable of achieving high-precision recognition and visual state management. In this study, we gathered both static and dynamic optical fiber data using a linewidth tunable light source (LTLS) and introduced a multimodal detection architecture that applies ensemble learning to the collected data. This constitutes what we believe to be the first field trial of concurrent recognition of optical fibers found both in co-cables and co-trenches. To identify co-cable fibers, we employed a double-layer cascaded Random Forest (DLC-RF) model based on the static features of fibers. For co-trench fiber, the dynamic characteristics of fiber vibrations are utilized in combination with multiple independent curve similarity contrast learners for classifying tasks. The proposed architecture is capable of automatically detecting the condition of the optical fiber and actively identifying the same routing segment within the network, eliminating the need for human intervention and enabling the visualization of passive optical fiber resources. Finally, after rigorous testing and validation across 11 sites in a typical urban area, including aggregation and backbone scenarios within the operator's live network environments, we have confirmed that the solution's ability to identify co-routes is accurate, exceeding 95%. This provides strong empirical evidence of its effectiveness.