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Dynamic polarization-insensitive BOTDA in direct-detection OFDM with CNN-based BFS extraction.

Di Qi, Jingyu Li, Xun Guan

    Optics Express
    |March 18, 2022
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    Summary

    This study introduces a dynamic polarization-insensitive Brillouin optical time domain analyzer (D/PI-BOTDA) for enhanced fiber optic sensing. It achieves accurate, rapid temperature measurements with improved Brillouin gain stability.

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    Area of Science:

    • Optical Engineering
    • Sensing Technology
    • Signal Processing

    Background:

    • Brillouin optical time domain analysis (BOTDA) is crucial for distributed fiber sensing.
    • Traditional BOTDA systems face challenges with polarization sensitivity and slow sensing speeds.
    • Accurate Brillouin frequency shift (BFS) extraction is vital for reliable measurements.

    Purpose of the Study:

    • To develop a dynamic, polarization-insensitive BOTDA (D/PI-BOTDA) system.
    • To enhance sensing speed and accuracy using orthogonal frequency division multiplexing (OFDM).
    • To improve the robustness of Brillouin gain measurements.

    Main Methods:

    • Implementation of a D/PI-BOTDA system utilizing intensity-modulated direct-detection (IM-DD).
    • Employment of a polarization-division-multiplexed (PDM) pump signal for polarization diversity.
    • Utilization of a multi-frequency OFDM probe signal for single-shot dynamic sensing.
    • Application of a Convolutional Neural Network (CNN) with SE-Res2Net for BFS extraction.

    Main Results:

    • Successful demonstration of distributed temperature sensing over 940 meters with a coefficient of 1.2°C/MHz.
    • Achieved a 4.38-fold suppression of Brillouin gain fluctuation compared to systems without polarization diversity.
    • The CNN-based BFS extraction method showed higher accuracy and 10x greater time efficiency than curve fitting.

    Conclusions:

    • The proposed D/PI-BOTDA system effectively overcomes polarization sensitivity and enables dynamic sensing.
    • OFDM and PDM techniques significantly improve sensing performance and data acquisition speed.
    • CNN-based BFS extraction offers a more accurate and efficient alternative to conventional methods.