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

TL;DR

This paper introduces a CNN-based method for real-time detection and characterization of reflective faults in fiber optic cables using noisy OTDR signals, significantly improving accuracy and reliability over traditional methods.

Contribution

It presents a novel deep learning approach that effectively detects and localizes fiber faults from noisy OTDR data, enhancing fault diagnosis in optical networks.

Findings

Higher detection capability with low false alarms

Improved localization accuracy at low SNR levels

Effective in noisy simulated OTDR environments

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 fault 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.