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

Shufan Jiang, Bangyan Lin, Yue Wu, Yuan Gao

TL;DR

Addressing autonomous management of digital-twin enabled 6G networks, the paper integrates large language models for intelligent data retrieval planning and automated radio resource management. It introduces LINKs, a two-stage, agentic LLM workflow that converts data plans into optimization problems using a parameter convertor and a solver (e.g., Gurobi/Pyomo) to satisfy $0 \\le p_i \\le p_{\\max}$ and $\\gamma_{bs} \\ge \\beta$ with $R_{ij} = B \\log_2(1 + \\gamma_{ij})$ and objective $\\min_{\\bm{p},\\bm{b}} \\(\\max_i \\frac{D_i}{\\sum_j b_{ij} R_{ij}}\\)$. It couples TimesFM-based cellular traffic load prediction (including zero-shot and LoRA-finetuned variants) to forecast demand and trigger data retrieval decisions via a lazy-loading strategy. Simulations over a 6 GHz UMa scenario with 10 CUEs and 10 Zigbee coordinators show high planning accuracy (average ~0.95) and near-optimal latency, validating the approach's potential for practical DT-6G deployment.

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.

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

TL;DR

Addressing autonomous management of digital-twin enabled 6G networks, the paper integrates large language models for intelligent data retrieval planning and automated radio resource management. It introduces LINKs, a two-stage, agentic LLM workflow that converts data plans into optimization problems using a parameter convertor and a solver (e.g., Gurobi/Pyomo) to satisfy and with and objective . It couples TimesFM-based cellular traffic load prediction (including zero-shot and LoRA-finetuned variants) to forecast demand and trigger data retrieval decisions via a lazy-loading strategy. Simulations over a 6 GHz UMa scenario with 10 CUEs and 10 Zigbee coordinators show high planning accuracy (average ~0.95) and near-optimal latency, validating the approach's potential for practical DT-6G deployment.

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.
Paper Structure (9 sections, 7 equations, 6 figures, 3 tables)

This paper contains 9 sections, 7 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. LLM Agentic Workflow for Network Management
  6. Data Retrieval and Radio Resource Management
  7. Cellular Traffic Load Prediction
  8. Simulation Results
  9. Conclusion

Figures (6)

  • Figure 1: System model of the proposed LLM-supported network management framework for 6G-empowered DT networks.
  • Figure 2: The overall workflow for LLM-based DT Management System
  • Figure 3: The detailed working principle of Parameter Convertor
  • Figure 4: Prompt design for Data Planning stage. The first stage of LINKs can be divided into four sub-parts from top to bottom, where, aside from initialization, the loop symbols within each sub-part denote potential iterations in the dialogue. On the right side, critical analysis and conclusions for example query in the chat are presented. Intermediate results are highlighted to clarify the outputs at each step of the first stage.
  • Figure 5: Accuracy variation trends with the number of Steps
  • ...and 1 more figures