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.

中文翻译：

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GainNet：协调生成式 AI 和 6G 网络的奇怪组合


人工智能生成内容（AIGC）的快速扩张反映了从辅助人工智能到生成人工智能（GAI）的迭代。同时，6G网络也将从万物互联向智能互联演进。然而，由于数据和资源的矛盾，他们似乎是一对奇怪的搭档。为了实现GAI和6G之间更好的协调互动，本文提出了面向GAI的云边端协同智能框架GAI-native Networks (GainNet)。 GainNet通过将GAI与6G网络设计深度结合，实现正向闭环知识流和可持续进化的GAI模型优化。在此基础上，提出了面向GAI的集成感知、通信和计算的通用资源编排机制（GaiRomISCC），以保证GainNet的高效运行。两个简单的案例研究证明了所提出方案的有效性和鲁棒性。最后，我们展望了 GAI 模型与 6G 网络之间相互作用的主要挑战和未来方向。