AI-Assisted OTDR for Real-Time Fault Localization in Long-Haul Fiber Networks
Abstract
The increasing nature of long-haul optical fiber networks necessitates effective and good fault localization system to maintain the continuous flow of data. OTDR has many years been the process by which faults have been detected, its operation however is limited by noisy traces and overlapping reflections as well as the random nature of optical channels. The paper outlines proposing an AI-based OTDR framework that would incorporate groundbreaking machine learning algorithms to improve the fault localization error and minimize the mean time to repair (MTTR). With the deep learning models, the system is used to preprocess, extract features, and classify OTDR signals to identify subtle anomalies that are not easily seen by traditional approaches. Using predictive analytics, the framework predicts the possibility of failure and contributes proactive maintenance live. It is experimentally shown that the proposed system can render significant improvements in the detection accuracy of faults, scalability, and resilience to the long-haul fiber networks and lead to the intelligent optical network management.