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The integration of a multitude of networked entities in 6G poses significant challenges for network design, resource allocation, optimization, and maintenance. The Network Digital Twin (NDT) offers a transformative approach to address these complexities, enabling predictive analytics and real-time decision-making. The Data-Driven Network Digital Twin Efficiency Assessment (DDNDTEA) presented in this study provides a comprehensive evaluation methodology for NDTs, ensuring network performance, scalability, and resilience. By incorporating real-time monitoring, data-driven optimization, simulation modeling, and economic analysis, DDNDTEA facilitates sustainable development and cost-effective network management in the 6G era.

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Data-Driven Network Digital Twin Efficiency Assessment

  • 会议论文

  • 发表日期：

    2025年

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  • Shoufeng Wang,
  • Ye Ouyang,
  • Jianchao Guo,
  • Fan Li,
  • Xuan Chen,
  • Lexi Xu,
  • Sen Bian,
  • Zhigang Wang,
  • Zhidong Ren,
  • Rongxing He

摘要

The integration of a multitude of networked entities in 6G poses significant challenges for network design, resource allocation, optimization, and maintenance. The Network Digital Twin (NDT) offers a transformative approach to address these complexities, enabling predictive analytics and real-time decision-making. The Data-Driven Network Digital Twin Efficiency Assessment (DDNDTEA) presented in this study provides a comprehensive evaluation methodology for NDTs, ensuring network performance, scalability, and resilience. By incorporating real-time monitoring, data-driven optimization, simulation modeling, and economic analysis, DDNDTEA facilitates sustainable development and cost-effective network management in the 6G era.