LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs
This addresses network management challenges for smart city applications, but it appears incremental as it applies existing LLMs to a new domain.
This paper tackles the problem of managing 6G-empowered digital twin networks by integrating large language models to optimize data retrieval and communication efficiency in smart cities, with simulation results showing performance improvements in data planning and network management.
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.