Generative AI for 6G IoT- Using Digital Twin as an Example

DOI

10.2991/978-94-6463-986-5_50How to use a DOI?

Keywords

Generative AI; 6G IoT; Digital Twin; Intelligent Radio; Network Optimization

Abstract

As 6G evolves towards high-density intelligent networks, integrating digital twins and generative AI is vital for 6G IoT. This paper explores generative AI in 6G Intelligent Radio digital twin. It first clarifies the synergy between intelligent radio and digital twin in 6G, and reveals how GAN, Diffusion Model, and RAG-LLM enhance data, optimize transmission, and generate policies. Generative AI outperforms traditional methods by 8.3% - 50% in resource allocation and noise resistance. The study also identifies challenges like physical-digital asynchrony and heavy edge-computing load, suggesting solutions. Finally, it anticipates generative AI’s applications in the metaverse and smart grid, guiding 6G IoT’s intelligent development. The significance of this research lies in its pioneering exploration of generative AI’s transformative potential for 6G IoT, bridging critical gaps between theoretical frameworks and practical implementations. By systematically addressing key challenges and demonstrating measurable performance improvements, the study establishes a foundational roadmap for future intelligent network evolution. Furthermore, it provides actionable insights for industry stakeholders to accelerate 6G standardization while fostering sustainable development of next-generation IoT ecosystems. The findings also highlight the strategic importance of AI-native network design in achieving global connectivity goals for emerging smart cities and Industry 5.0 applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Fangyu Liu
PY  - 2026
DA  - 2026/02/18
TI  - Generative AI for 6G IoT- Using Digital Twin as an Example
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 483
EP  - 490
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_50
DO  - 10.2991/978-94-6463-986-5_50
ID  - Liu2026
ER  -