Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals

Conference: Photonische Netze - 22. ITG-Fachtagung
05/19/2021 - 05/20/2021 at Online

Proceedings: ITG-Fb. 297: Photonische Netze

Pages: 5Language: englishTyp: PDF

Authors:
Abdelli, Khouloud (Advanced Technology & Chair of Communications ADVA, Munich & Kiel University, Kiel, Germany)
Griesser, Helmut (Advanced Technology ADVA, Optical Networking SE, Munich, Germany)
Pachnicke, Stephan (Chair of Communications, Kiel University, Kiel, Germany)

Abstract:
Fast and accurate fault detection and localization in fiber optic cables is extremely important to ensure the optical network survivability and reliability. Hence there exists a crucial need to develop an automatic and reliable algorithm for real-time optical fiber faults’ detection and diagnosis leveraging the telemetry data obtained by an optical time domain reflectometry (OTDR) instrument. In this paper, we propose a novel data-driven approach based on convolutional neural networks (CNNs) to detect and characterize the fiber reflective faults given noisy simulated OTDR data, whose SNR (signal-to-noise ratio) values vary from 0 dB to 30 dB, incorporating reflective event patterns. In our simulations, we achieved a higher detection capability with low false alarm rate and greater localization accuracy even for low SNR values compared to conventionally employed techniques.