The future networks pose 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 AIGN (AI-Generated Network), a novel intention-driven paradigm for network design, 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, AIGN 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 AIGN can effectively guide the design of transmit power allocation in digital twin-based access networks.

翻译：未来网络对智能化和定制化设计提出了强烈需求，以应对不断增长的网络规模、动态变化的环境、多样化的用户需求以及复杂的人工配置。然而，传统的基于规则的解决方案严重依赖人工经验和专业知识，而数据驱动的智能算法仍缺乏可解释性和泛化能力。本文提出了一种新颖的意图驱动网络设计范式——AIGN（AI生成网络），使运营商能够快速生成多种定制化网络解决方案，并实现无专家参与的问题优化。基于扩散模型的学习方法驱动下，AIGN具有学习奖励最大化轨迹、自动满足多重约束、适应不同目标和场景的巨大潜力，甚至能够智能地创造现有网络环境中未曾出现的新型设计和机制。最后，我们通过一个用例证明，AIGN能够有效指导数字孪生接入网络中发射功率分配的设计。