GainNet: Coordinates the Odd Couple of Generative AI and 6G Networks (2401.02662v1)
Abstract: The rapid expansion of AI-generated content (AIGC) reflects the iteration from assistive AI towards generative AI (GAI) with creativity. Meanwhile, the 6G networks will also evolve from the Internet-of-everything to the Internet-of-intelligence with hybrid heterogeneous network architectures. In the future, the interplay between GAI and the 6G will lead to new opportunities, where GAI can learn the knowledge of personalized data from the massive connected 6G end devices, while GAI's powerful generation ability can provide advanced network solutions for 6G network and provide 6G end devices with various AIGC services. 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 paper. 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 (GaiRom-ISCC) 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.
- H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, D. Kim and others, “Enabling AI-generated content (AIGC) services in wireless edge networks,” arXiv preprint arXiv:2301.03220, 2023.
- H. Du, R. Zhang, D. Niyato, J. Kang, Z. Xiong, D. Kim, X. Shen, Xuemin H. Poor, “Exploring collaborative distributed diffusion-based AI-generated content (AIGC) in wireless networks,” IEEE Network, no. 99, pp. 1-8, 2023.
- Y. Huang, M. Xu, X. Zhang, D. Niyato, Z. Xiong, S. Wang, T. Huang, “AI-Generated 6G Internet Design: A Diffusion Model-based Learning Approach,” arXiv preprint arXiv:2303.13869, 2023.
- X. Huang, P. Li, H. Du, J. Kang, D. Niyato, D. Kim, Y. Wu, Yuan, “Federated Learning-Empowered AI-Generated Content in Wireless Networks,” arXiv preprint arXiv:2307.07146, 2023.
- H. Zou, Q. Zhao, L. Bariah, M. Bennis, M. Debbah, “Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence,” arXiv preprint arXiv:2307.02757, 2023.
- W. Zhuang, C. Chen, L. Lyu, “When foundation model meets federated learning: Motivations, challenges, and future directions,” arXiv preprint arXiv:2306.15546, 2023.
- S. Duan, D. Wang, J. Ren, F. Lyu, Y. Zhang, H. Wu, X. Shen, “Distributed artificial intelligence empowered by end-edge-cloud computing: A survey,” IEEE Communications Surveys & Tutorials, 2022.
- X. You, C. Wang, J. Huang, X. Gao, Z. Zhang, M. Wang, Y. Huang, C. Zhang, Y. Jiang, J. Wang and others, “Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts,” in Science China Information Sciences, vol. 64, pp. 1-74, 2021.
- G. Zhu, Z. Lyu, X. Jiao, P. Liu, M. Chen, J. Xu, S. Cui, P. Zhang, “Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G,” in Science China Information Sciences, vol. 66, no. 3, pp. 130301, 2023.
- Y. Tian, Y. Wan, L. Lyu, D. Yao, H. Jin, L. Sun, “FedBERT: When federated learning meets pre-training,” in ACM Transactions on Intelligent Systems and Technology (TIST), vol. 13, no. 4, pp. 1-26, 2022.
- Z. Lin, G. Qu, X. Chen, K. Huang, Kaibin, “Split Learning in 6G Edge Networks,” arXiv preprint arXiv:2306.12194, 2023.
- Y. Cao, S. Li, Y. Liu, Z. Yan, Y. Dai, P. Yu, L. Sun, “A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt,” arXiv preprint arXiv:2303.04226, 2023.
- Y. Chen, R. Li, Z. Zhao, C. Peng, J. Wu, E. Hossain, H. Zhang, “NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services,” arXiv preprint arXiv:2307.06148, 2023.
- N. Chen, Z. Cheng, X. Fan, B. Huang, X. Du, and G. Mohsen, “Integrated Sensing, Communication, and Computing for Cost-effective Multimodal Federated Perception,” arXiv preprint arXiv:2311.03815, 2023.
- F. Dernoncourt, J. Lee, “Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts,” arXiv preprint arXiv:1710.06071, 2017.
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