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In the rapidly evolving landscape of wireless networks, parameters play a crucial role in ensuring optimal performance and user experience. Traditional static optimization methods face challenges due to the complex and dynamic nature of modern networks. This paper explores an innovative approach to network parameter self-healing by integrating digital twin and AI technologies. We propose an AI-driven RAN agent capable of scenario-based network optimization, enhancing both operational efficiency and user satisfaction.

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Research on Self-Healing Wireless Network Parameters Based on Digital Twin and AI Technologies

  • 会议论文

  • 发表日期：

    2026年

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  • Jing Liu,
  • Kai Meng,
  • Kun Zhu,
  • Xiang Huan

摘要

In the rapidly evolving landscape of wireless networks, parameters play a crucial role in ensuring optimal performance and user experience. Traditional static optimization methods face challenges due to the complex and dynamic nature of modern networks. This paper explores an innovative approach to network parameter self-healing by integrating digital twin and AI technologies. We propose an AI-driven RAN agent capable of scenario-based network optimization, enhancing both operational efficiency and user satisfaction.