GainNet: Coordinates the Odd Couple of Generative AI and 6G Networks

Ning Chen, Jie Yang, Zhipeng Cheng, Xuwei Fan, Zhang Liu, Bangzhen Huang, Yifeng Zhao, Lianfen Huang, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The rapid expansion of AI-generated content (AIGC) reflects the iteration from assistive AI towards generative AI (GAI). Meanwhile, the 6G networks will also evolve from the Internet-of-Everything to the Internet-of-Intelligence. However, they seem to be an odd couple, due to the contradiction of data and resources. To achieve a better-coordinated interplay between GAI and 6G, the GAI-native Networks (GainNet), a GAI-oriented collaborative cloud-edge-end intelligence framework, is proposed in this article. By deeply integrating GAI with 6G network design, GainNet realizes the positive closed-loop knowledge flow and sustainable-evolution GAI model optimization. On this basis, the GAI-oriented generic Resource Orchestration Mechanism with Integrated Sensing, Communication, and Computing (GaiRomISCC) is proposed to guarantee the efficient operation of GainNet. Two simple case studies demonstrate the effectiveness and robustness of the proposed schemes. Finally, we envision the key challenges and future directions concerning the interplay between GAI models and 6G networks.

Original languageEnglish
Pages (from-to)56-65
Number of pages10
JournalIEEE Network
Volume38
Issue number5
DOIs
StatePublished - 2024

Keywords

  • 6G
  • collaborative cloud-edge-end intelligence
  • communication
  • computing
  • generative AI
  • integrated sensing
  • resource orchestration

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