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MFCA-Transformador: Reconocimiento de señales de modulación basado en la fusión de características multidimensionales

Xiao Hu1,2, Mingju Chen1,2, Xingyue Zhang1,2

  • 1School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644005, China.

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PubMed
Resumen

Este estudio introduce una nueva red de características multidimensionales para el reconocimiento de señales de modulación, mejorando la precisión en entornos de baja relación señal-ruido. El transformador MFCA mejora la fusión e interacción de características, logrando un rendimiento superior a los métodos de aprendizaje profundo existentes.

Palabras clave:
Mecanismo de atenciónextracción de característicasreconocimiento de modulaciónFusión de características multidimensionales

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Área de la Ciencia:

  • Procesamiento de señales
  • Aprendizaje automático
  • Inteligencia artificial

Sus antecedentes:

  • La baja relación señal-ruido (SNR) plantea desafíos para el reconocimiento de la señal de modulación, lo que lleva a una mala extracción y precisión de las características.
  • Los métodos existentes a menudo se basan en datos monomodales, lo que limita las capacidades de reconocimiento de señales complejas.

Objetivo del estudio:

  • Proponer una red de transformadores MFCA de características multidimensionales para el reconocimiento robusto de la señal de modulación.
  • Mejorar la fusión de características y la interacción de información entre canales para una mayor precisión.

Principales métodos:

  • Integración de la información de fase, frecuencia y potencia en una red de características multidimensional.
  • Utilizando la fusión de características dinámicas triples (TDFF) para la fusión de características adaptativas.
  • Emplear un módulo de Atención Convolucional Previo al Canal (CPCA) para mejorar la comunicación entre canales.
  • Incorporar el suavizado de etiquetas en la función de pérdida para mejorar la generalización del modelo.

Principales resultados:

  • La red de transformadores MFCA propuesta mejora significativamente la precisión del reconocimiento en conjuntos de datos públicos.
  • Logró una precisión de reconocimiento de hasta un 93,2% con un SNR alto, superando los métodos de aprendizaje profundo existentes en un 3-14%.
  • Ha demostrado una mayor capacidad para manejar características complejas y reducir el exceso de ajuste.

Conclusiones:

  • La red de transformadores MFCA ofrece una solución superior para el reconocimiento de la señal de modulación, especialmente en condiciones de baja SNR.
  • La integración de características multidimensionales y módulos avanzados como TDFF y CPCA es efectiva para el análisis de señales complejas.