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6G comprehensive intelligence: network operations and optimization based on Large Language Models

Sifan Long, Fengxiao Tang, Yangfan Li, Tiao Tan, Zhengjie Jin, Ming Zhao, Nei Kato

TL;DR

This work addresses the need for comprehensive intelligence in 6G networks to support perception, cognition, security, and personalized services. It proposes an LLM-based network health assessment and performance optimization architecture built around a Cloud-Edge-Device collaboration, with a data layer, a large language model base, a function pool, a logic layer, and task-scene modeling. The paper demonstrates a drone-health-demo within this framework, leveraging digital twin data, tree-structured semantic generation, and knowledge graphs to produce health reports and decision support, while discussing open issues such as generalization, multimodal fusion, and security. The proposed approach aims to enable proactive fault diagnosis, real-time performance optimization, and adaptable resource scheduling, offering a practical pathway toward intelligent, secure, and efficient 6G networks.

Abstract

The sixth generation mobile communication standard (6G) can promote the development of Industrial Internet and Internet of Things (IoT). To achieve comprehensive intelligent development of the network and provide customers with higher quality personalized services. This paper proposes a network performance optimization and intelligent operation network architecture based on Large Language Model (LLM), aiming to build a comprehensive intelligent 6G network system. The Large Language Model, with more parameters and stronger learning ability, can more accurately capture patterns and features in data, which can achieve more accurate content output and high intelligence and provide strong support for related research such as network data security, privacy protection, and health assessment. This paper also presents the design framework of a network health assessment system based on LLM and focuses on its potential application value, through the case of network health management system, it is fully demonstrated that the 6G intelligent network system based on LLM has important practical significance for the comprehensive realization of intelligence.

6G comprehensive intelligence: network operations and optimization based on Large Language Models

TL;DR

This work addresses the need for comprehensive intelligence in 6G networks to support perception, cognition, security, and personalized services. It proposes an LLM-based network health assessment and performance optimization architecture built around a Cloud-Edge-Device collaboration, with a data layer, a large language model base, a function pool, a logic layer, and task-scene modeling. The paper demonstrates a drone-health-demo within this framework, leveraging digital twin data, tree-structured semantic generation, and knowledge graphs to produce health reports and decision support, while discussing open issues such as generalization, multimodal fusion, and security. The proposed approach aims to enable proactive fault diagnosis, real-time performance optimization, and adaptable resource scheduling, offering a practical pathway toward intelligent, secure, and efficient 6G networks.

Abstract

The sixth generation mobile communication standard (6G) can promote the development of Industrial Internet and Internet of Things (IoT). To achieve comprehensive intelligent development of the network and provide customers with higher quality personalized services. This paper proposes a network performance optimization and intelligent operation network architecture based on Large Language Model (LLM), aiming to build a comprehensive intelligent 6G network system. The Large Language Model, with more parameters and stronger learning ability, can more accurately capture patterns and features in data, which can achieve more accurate content output and high intelligence and provide strong support for related research such as network data security, privacy protection, and health assessment. This paper also presents the design framework of a network health assessment system based on LLM and focuses on its potential application value, through the case of network health management system, it is fully demonstrated that the 6G intelligent network system based on LLM has important practical significance for the comprehensive realization of intelligence.
Paper Structure (16 sections, 4 figures)

This paper contains 16 sections, 4 figures.

Table of Contents

  1. Introduction
  2. The background of Large Language Models and related applications in the network
  3. Large Language Models
  4. Related Work of LLM in the Network
  5. Intelligent network architecture
  6. Our demo
  7. Several open issues
  8. Model generalization ability and robustness
  9. Data understanding and representation
  10. Multimodal alignment problem
  11. Selection and equilibrium of LLM and small model
  12. Model self evolution problem
  13. Data collection and real-time performance of the system
  14. Security and interpretability of LLM in networks
  15. Conclusion
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Application of Large Language Model in intelligent network operations and performance optimization.
  • Figure 2: The development timeline of the typical representative LLM framework.
  • Figure 3: Overall design scheme for network health assessment and performance optimization architecture based on LLM.
  • Figure 5: Schematic diagram of network health management system architecture based on textual proposition learning and LLM.