Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Large Language Models and Artificial Intelligence Generated Content Technologies Meet Communication Networks

Jie Guo, Meiting Wang, Hang Yin, Bin Song, Yuhao Chi, Fei Richard Yu, Chau Yuen

TL;DR

This work aims to deepen the understanding of how LLMs and AIGC can synergize with and enhance the development of advanced intelligent communication networks, contributing to a more profound comprehension of next-generation intelligent communication networks.

Abstract

Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted great interests from both academia and industry. Although some noteworthy advancements have been made in this area, a comprehensive exploration of the intricate relationship between AIGC and communication networks remains relatively limited. To address this issue, this paper conducts an exhaustive survey from dual standpoints: firstly, it scrutinizes the integration of LLMs and AIGC technologies within the domain of communication networks; secondly, it investigates how the communication networks can further bolster the capabilities of LLMs and AIGC. Additionally, this research explores the promising applications along with the challenges encountered during the incorporation of these AI technologies into communication networks. Through these detailed analyses, our work aims to deepen the understanding of how LLMs and AIGC can synergize with and enhance the development of advanced intelligent communication networks, contributing to a more profound comprehension of next-generation intelligent communication networks.

Large Language Models and Artificial Intelligence Generated Content Technologies Meet Communication Networks

TL;DR

This work aims to deepen the understanding of how LLMs and AIGC can synergize with and enhance the development of advanced intelligent communication networks, contributing to a more profound comprehension of next-generation intelligent communication networks.

Abstract

Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted great interests from both academia and industry. Although some noteworthy advancements have been made in this area, a comprehensive exploration of the intricate relationship between AIGC and communication networks remains relatively limited. To address this issue, this paper conducts an exhaustive survey from dual standpoints: firstly, it scrutinizes the integration of LLMs and AIGC technologies within the domain of communication networks; secondly, it investigates how the communication networks can further bolster the capabilities of LLMs and AIGC. Additionally, this research explores the promising applications along with the challenges encountered during the incorporation of these AI technologies into communication networks. Through these detailed analyses, our work aims to deepen the understanding of how LLMs and AIGC can synergize with and enhance the development of advanced intelligent communication networks, contributing to a more profound comprehension of next-generation intelligent communication networks.

Paper Structure

This paper contains 51 sections, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background of LLMs and AIGC
  3. Definition of AIGC
  4. Structure of LLMs
  5. Characteristics of LLMs
  6. Classification of LLMs
  7. Other AIGC Technologies
  8. Disadvantages of LLMs and AIGC
  9. Lessons Learned
  10. LLMs and AIGC Technologies for Communication Networks
  11. Data Augmentation for Source and Destination Nodes
  12. Unimodal Data Volume Expansion
  13. Cross-Modal Data Supplementation
  14. Generation of Communication Scenarios and Network Configuration
  15. Channel Modeling
  16. ...and 36 more sections

Figures (9)

  • Figure 1: Organization structure of this paper.
  • Figure 2: Schematics of the main structures in different LMs and corresponding applications in the communication field.
  • Figure 3: The classification and utilization of AIGC models in communication networks.
  • Figure 4: Channel modeling and scenario generation with LLMs (taking the traffic scenario as an example).
  • Figure 5: A semantic communication system combining with LLMs and AIGC technologies.
  • ...and 4 more figures