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Rendimiento del Nuevo Esquema de Bloqueo de Acceso Reactivo para Sistemas NB-IoT Basado en Inferencia de Aprendizaje
Anastasia Daraseliya1, Eduard Sopin1,2, Julia Kolcheva1
1Department of Probability Theory and Cyber Security, Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., Moscow 117198, Russia.
Sensors (Basel, Switzerland)
|January 28, 2026
Resumen
Este estudio presenta un esquema de bloqueo de acceso basado en aprendizaje automático para sistemas NB-IoT. El nuevo método mejora el rendimiento y gestiona el retraso en redes de área amplia de baja potencia (LPWAN) ajustando dinámicamente la carga de tráfico.
Sus antecedentes:
- Las tecnologías modernas de redes de área amplia de baja potencia (LPWAN) de grado 5G+, como Narrowband Internet-of-Things (NB-IoT), utilizan ALOHA ranurado multicanal para el acceso aleatorio.
- La fase de acceso aleatorio en estos sistemas sufre de bajo rendimiento e inestabilidad debido a las fluctuaciones del tráfico.
- Mantener un rendimiento óptimo es un desafío porque la estación base (BS) carece de conocimiento de la carga de tráfico ofrecida actual.
Conclusiones:
- El esquema propuesto de bloqueo de acceso reactivo basado en ML mejora eficazmente el rendimiento del sistema NB-IoT.
- La conciencia dinámica del tráfico y el control proactivo de la congestión garantizan un retraso limitado y evitan la sobrecarga del sistema.
- Este enfoque ofrece una solución robusta para la operación estable y eficiente de LPWAN en condiciones de tráfico fluctuantes.
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