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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Vortex beams enhance channel capacity in underwater wireless optical communication (UWOC).
    • Oceanic turbulence (OT) severely distorts vortex beams, degrading UWOC quality.
    • Accurate identification of orbital angular momentum (OAM) modes and distortion correction are vital for robust UWOC.

    Purpose of the Study:

    • To develop a joint distortion correction and OAM mode identification method for UWOC systems.
    • To experimentally validate the proposed approach using a Siamese network (SN).
    • To enable accurate OAM mode identification and Zernike coefficient prediction under limited data.

    Main Methods:

    • A Siamese network (SN) architecture was employed for feature extraction and fusion from phase screens and intensity patterns.
    • A classification network was integrated within the SN for simultaneous OAM mode identification and Zernike polynomial coefficient prediction.
    • The approach was designed to function effectively with a limited number of training samples.

    Main Results:

    • The SN accurately identified four OAM modes and predicted Zernike coefficients for four levels of oceanic turbulence (OT).
    • Reconstructed phase screens using predicted coefficients facilitated high-quality correction of distorted vortex beams.
    • The SN demonstrated robust generalization performance, even with limited sample data.

    Conclusions:

    • The proposed SN-based method offers an effective solution for correcting vortex beam distortions in UWOC.
    • This approach enables reliable OAM mode identification and turbulence mitigation in challenging underwater environments.
    • The study presents a novel pathway for improving the performance and reliability of UWOC systems.