@techreport{li-nmrg-dtn-data-generation-optimization-04,
    number =    {draft-li-nmrg-dtn-data-generation-optimization-04},
    type =      {Internet-Draft},
    institution =   {Internet Engineering Task Force},
    publisher = {Internet Engineering Task Force},
    note =      {Work in Progress},
    url =       {https://datatracker.ietf.org/doc/draft-li-nmrg-dtn-data-generation-optimization/04/},
    author =    {Mei Li and Cheng Zhou and Danyang Chen and Qin Wu and YUANYUANYANG},
    title =     {{Data Generation and Optimization for Network Digital Twin}},
    pagetotal = 13,
    year =      2025,
    month =     jul,
    day =       7,
    abstract =  {Network Digital Twin (NDT) can be used as a secure and cost-effective environment for network operators to evaluate network in various what-if scenarios. Recently, Artificial Intelligence (AI) models, especially neural networks, have been applied for NDT modeling. The quality of deep learning models mainly depends on two aspects: model architecture and data. This memo focuses on how to improve the model quality from the data perspective.},
}