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    Este estudio presenta una red neuronal siamesa para corregir haces de vórtice distorsionados e identificar modos de momento angular orbital (OAM) en comunicaciones ópticas inalámbricas submarinas (UWOC). El método mitiga eficazmente los efectos de la turbulencia oceánica (OT), mejorando el rendimiento del sistema UWOC.

    Palabras clave:
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    Área de la Ciencia:

    • Comunicaciones Ópticas
    • Procesamiento de Señales
    • Aprendizaje Automático

    Sus antecedentes:

    • Los haces de vórtice mejoran la capacidad del canal en las comunicaciones ópticas inalámbricas submarinas (UWOC).
    • La turbulencia oceánica (OT) distorsiona severamente los haces de vórtice, degradando la calidad de UWOC.
    • La identificación precisa de los modos de momento angular orbital (OAM) y la corrección de la distorsión son vitales para una UWOC robusta.

    Objetivo del estudio:

    • Desarrollar un método conjunto de corrección de distorsión e identificación de modos OAM para sistemas UWOC.
    • Validar experimentalmente el enfoque propuesto utilizando una red neuronal siamesa (SN).
    • Permitir la identificación precisa de modos OAM y la predicción de coeficientes de Zernike con datos limitados.

    Principales métodos:

    • Se empleó una arquitectura de red neuronal siamesa (SN) para la extracción y fusión de características de pantallas de fase y patrones de intensidad.
    • Se integró una red de clasificación dentro de la SN para la identificación simultánea de modos OAM y la predicción de coeficientes de polinomio de Zernike.
    • El enfoque se diseñó para funcionar eficazmente con un número limitado de muestras de entrenamiento.

    Principales resultados:

    • La SN identificó con precisión cuatro modos OAM y predijo los coeficientes de Zernike para cuatro niveles de turbulencia oceánica (OT).
    • Las pantallas de fase reconstruidas utilizando los coeficientes predichos facilitaron la corrección de alta calidad de los haces de vórtice distorsionados.
    • La SN demostró un rendimiento de generalización robusto, incluso con datos de muestra limitados.

    Conclusiones:

    • El método propuesto basado en SN ofrece una solución eficaz para corregir las distorsiones de los haces de vórtice en UWOC.
    • Este enfoque permite la identificación fiable de modos OAM y la mitigación de la turbulencia en entornos submarinos desafiantes.
    • El estudio presenta una vía novedosa para mejorar el rendimiento y la fiabilidad de los sistemas UWOC.