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Descarga de tareas asistida por múltiples UAV y optimización de la trayectoria para la computación de borde a través

Jiajia Liu1, Haoran Hu2, Xu Bai2

  • 1Faculty Development and Teaching Evaluation Center, Civil Aviation Flight University of China, Guanghan 618307, China.

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Resumen
Este resumen es generado por máquina.

Este estudio introduce una red de computación de borde móvil (MEC) de vehículos aéreos no tripulados (UAV) que utiliza acceso múltiple no ortogonal (NOMA) para reducir los retrasos en las colas de tareas. La estrategia propuesta reduce significativamente la latencia del sistema al optimizar la descarga de tareas y las trayectorias de los UAV.

Palabras clave:
El NOMAVehículos aéreos no tripuladosComputación de bordedescarga de la tareaOptimización de trayectoria

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Área de la Ciencia:

  • Comunicaciones inalámbricas
  • Computación de borde móvil (MEC)
  • Optimización de la red

Sus antecedentes:

  • Los vehículos aéreos no tripulados (UAV) ofrecen un despliegue flexible para mejorar los sistemas de computación de borde móvil (MEC).
  • Las colas de tareas y el desequilibrio de la carga de red en MEC aumentan los retrasos de espera de los usuarios.
  • Las soluciones existentes luchan con cargas de tareas dinámicas y distribución desigual de servicios.

Objetivo del estudio:

  • Proponer un modelo colaborativo de red MEC multi-UAV para mitigar las colas de transmisión y el desequilibrio de carga.
  • Reducir el retraso general del sistema en las redes MEC optimizando la descarga de tareas y las trayectorias de los UAV.
  • Mejorar la cobertura de las comunicaciones inalámbricas y la calidad del servicio mediante la descarga dinámica de tareas de los VANT.

Principales métodos:

  • Desarrolló una arquitectura de red MEC colaborativa multi-UAV que integra el acceso múltiple no ortogonal (NOMA).
  • Formulado un problema de optimización de la estrategia de descarga de tareas teniendo en cuenta las limitaciones de retraso y consumo de energía.
  • Diseñó una estrategia de descarga optimizada para el retraso utilizando el algoritmo Twin Delayed Deep Deterministic Policy Gradient (TD3).

Principales resultados:

  • La estrategia propuesta basada en el TD3 reduce significativamente el retraso general del sistema en comparación con los métodos tradicionales.
  • Reducción del retraso del 9,8% al 20,2% en varios escenarios (volumen de tareas, número de dispositivos, velocidad/tiempo de los UAV, capacidades informáticas).
  • Se ha demostrado un equilibrio de carga eficaz y se han reducido los retrasos en las colas gracias a la descarga dinámica de tareas entre los UAV.

Conclusiones:

  • La red MEC colaborativa multi-UAV propuesta con optimización basada en NOMA y TD3 es efectiva para minimizar el retraso del sistema.
  • La descarga dinámica de tareas y las trayectorias optimizadas de los UAV son cruciales para mejorar el rendimiento de MEC.
  • La solución ofrece un enfoque sólido para abordar los desafíos en entornos de computación de borde de alta demanda.