Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro, Antonio Napoli, Sasipim Srivallapanondh, Sergei K. Turitsyn, Pedro Freire
arXiv:2602.10401v1h-index: 25
Originality Incremental advance
AI Analysis
This addresses failure detection in optical networks, offering a significant gain but likely incremental as it builds on existing online learning methods.
The paper tackled the problem of concept drift in optical network failure detection by developing an online learning-based approach, resulting in up to a 70% performance improvement over static models with low latency.
We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventional static models while maintaining low latency.