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Un esquema de asignación de recursos basado en ML para la optimización de la energía en 5G NR

Xiao Yao1, Antonio Pérez Yuste1

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Este estudio introduce un marco de aprendizaje automático para optimizar el consumo de energía de 5G New Radio. Reduce el consumo de energía en más del 40%, al tiempo que garantiza la calidad del servicio mediante la gestión predictiva de los recursos.

Palabras clave:
5G RANRRC y sus derivadoseficiencia energéticaAprendizaje automáticoasignación de recursos

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Área de la Ciencia:

  • Ingeniería de las telecomunicaciones
  • Ciencias de la computación
  • Inteligencia artificial

Sus antecedentes:

  • Las redes 5G New Radio (5G NR) se enfrentan a una creciente demanda de energía.
  • La gestión eficiente de la energía es crucial para el funcionamiento sostenible de la red.
  • El mantenimiento de la calidad del servicio (QoS) junto con el ahorro de energía presenta un desafío importante.

Objetivo del estudio:

  • Proponer y validar un marco de optimización de la energía basado en el aprendizaje automático para 5G NR.
  • Reducir el consumo de energía en las estaciones base 5G NR.
  • Asegurar que las estrategias de reducción de la energía no comprometan los parámetros esenciales de calidad de servicio.

Principales métodos:

  • Utilización de un algoritmo de árbol de clasificación y regresión (CART) para la previsión predictiva de la carga.
  • Implementación de la reconfiguración dinámica de los recursos de la célula basada en la carga prevista de la red.
  • Simulación de un diseño de red de conectividad dual entre bandas NR-NR para su validación.

Principales resultados:

  • Se logró una reducción del 42,3% en el consumo de energía.
  • Parámetros de Calidad de Servicio (QoS) mantenidos dentro de los umbrales especificados por el Proyecto de Asociación de Tercera Generación (3GPP).
  • Validación cuantitativa del modelo propuesto mediante un estudio de caso de red simulado.

Conclusiones:

  • El marco de aprendizaje automático propuesto optimiza efectivamente el consumo de energía en las redes 5G NR.
  • La reconfiguración dinámica de los recursos de la célula impulsada por la previsión predictiva de la carga es una estrategia viable para el ahorro de energía.
  • El algoritmo CART proporciona un método sólido para lograr reducciones significativas de energía sin sacrificar el rendimiento de la red.