The 6G Internet poses intense demands for intelligent and customized designs to cope with the surging network scale, dynamically time-varying environments, diverse user requirements, and complicated manual configuration. However, traditional rule-based solutions heavily rely on human efforts and expertise, while data-driven intelligent algorithms still lack interpretability and generalization. In this paper, we propose the AIGI (AI-Generated Internet), a novel intention-driven design paradigm for the 6G Internet, which allows operators to quickly generate a variety of customized network solutions and achieve expert-free problem optimization. Driven by the diffusion model-based learning approach, AIGI has great potential to learn the reward-maximizing trajectories, automatically satisfy multiple constraints, adapt to different objectives and scenarios, or even intelligently create novel designs and mechanisms unseen in existing network environments. Finally, we conduct a use case to demonstrate that AIGI can effectively guide the design of transmit power allocation in digital twin-based 6G networks.

翻译：Translated Abstract: 6G互联网对于智能化和个性化设计提出了巨大的需求，以适应不断扩大的网络规模，动态时变的环境，多样化的用户需求和复杂的手动配置。然而，传统的基于规则的解决方案严重依赖于人类的努力和专业知识，而基于数据驱动的智能算法仍然缺乏可解释性和泛化能力。本文提出了AIGI（人工智能生成的互联网），这是一种面向6G互联网的新的意愿驱动设计范式，可以让运营商快速生成各种个性化的网络解决方案，实现无需专家参与的问题优化。AIGI采用基于扩散模型的学习方法，具有很大的潜力，可以学习最大化回报的轨迹，自动满足多个约束条件，适应不同的目标和场景，甚至可以智能地创建在现有网络环境中看不到的新的设计和机制。最后，我们进行了一个用例，以演示AIGI如何有效指导基于数字孪生的6G网络中的发送功率分配设计。