Beyond the Limitation of Pulse Width in Optical Time-domain Reflectometry

TL;DR

This paper introduces a deep convolutional neural network for OTDR signal deconvolution, significantly improving spatial resolution and signal-to-noise ratio over traditional methods, enabling more accurate fiber event detection.

Contribution

It presents a novel deep learning approach for OTDR deconvolution that outperforms conventional algorithms in accuracy and noise handling.

Findings

Neural network achieves higher deconvolution accuracy.

Improved signal-to-noise ratio in OTDR measurements.

Enhanced spatial resolution in fiber sensing.

Abstract

Optical time-domain reflectometry (OTDR) is the basis for distributed time-domain optical fiber sensing techniques. By injecting pulse light into an optical fiber, the distance information of an event can be obtained based on the time of light flight. The minimum distinguishable event separation along the fiber length is called the spatial resolution, which is determined by the optical pulse width. By reducing the pulse width, the spatial resolution can be improved. However, at the same time, the signal-to-noise ratio of the system is degraded, and higher speed equipment is required. To solve this problem, data processing methods such as iterative subdivision, deconvolution, and neural networks have been proposed. However, they all have some shortcomings and thus have not been widely applied. Here, we propose and experimentally demonstrate an OTDR deconvolution neural network based on deep convolutional neural networks. A simplified OTDR model is built to generate a large amount of training data. By optimizing the network structure and training data, an effective OTDR deconvolution is achieved. The simulation and experimental results show that the proposed neural network can achieve more accurate deconvolution than the conventional deconvolution algorithm with a higher signal-to-noise ratio.