LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs

TL;DR

This paper introduces LINKs, a framework that uses large language models to optimize data management and radio resource allocation in 6G-enabled digital twin networks, improving efficiency in smart city applications.

Contribution

It presents a novel LLM-based management framework for 6G digital twins, including a lazy loading strategy and an optimization approach for network resource management.

Findings

Reduced transmission delay through lazy data loading

Enhanced network management efficiency in simulations

Effective LLM-based optimization for RRM tasks

Abstract

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.