用大模型自动管理6G数字孪生网络，提升城市智能通信效率
LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs
- 大模型驱动的懒加载策略，按需调取数据降低延迟
- 将数据检索转化为数值优化问题，实现高效无线资源管理
- 全自治管理框架，适合智能城市等复杂网络场景
在快速发展的数字孪生（DT）与6G网络背景下，大语言模型（LLMs）为网络管理提供了新思路。本文研究了LLMs在6G赋能的数字孪生网络中的应用，重点优化智慧城市场景下的数据检索与通信效率。提出的框架LINKs通过智能分析数字孪生问题并实现无线资源管理（RRM）的完全自治，无需人工干预。该框架采用懒加载策略，仅选择性地获取相关数据，以最小化传输延迟。基于数据检索计划，LLMs将检索任务转化为数值优化问题，并利用求解器构建最优的无线资源管理方案，确保网络通信高效。仿真结果表明，该方法在数据规划和网络管理方面均有显著性能提升，验证了大模型在融合数字孪生与6G技术中的潜力。
原文摘要 · Abstract (English)
In the rapidly evolving landscape of digital twins (DT) and 6G networks, the integration of large language models (LLMs) presents a novel approach to network management. This paper explores the application of LLMs in managing 6G-empowered DT networks, with a focus on optimizing data retrieval and communication efficiency in smart city scenarios. The proposed framework leverages LLMs for intelligent DT problem analysis and radio resource management (RRM) in fully autonomous way without any manual intervention. Our proposed framework -- LINKs, builds up a lazy loading strategy which can minimize transmission delay by selectively retrieving the relevant data. Based on the data retrieval plan, LLMs transform the retrieval task into an numerical optimization problem and utilizing solvers to build an optimal RRM, ensuring efficient communication across the network. Simulation results demonstrate the performance improvements in data planning and network management, highlighting the potential of LLMs to enhance the integration of DT and 6G technologies.
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