Digital twin, which enables emulation, evaluation, and optimization of physical entities through synchronized digital replicas, has gained increasing attention as a promising technology for intricate wireless networks. For 6G, numerous innovative wireless technologies and network architectures have posed new challenges in establishing wireless network digital twins. To tackle these challenges, artificial intelligence (AI), particularly the flourishing generative AI, emerges as a potential solution. In this article, we discuss emerging prerequisites for wireless network digital twins considering the complicated network architecture, tremendous network scale, extensive coverage, and diversified application scenarios in the 6G era. We further explore the applications of generative AI, such as Transformer and diffusion model, to empower the 6G digital twin from multiple perspectives including physical-digital modeling, synchronization, and slicing capability. Subsequently, we propose a hierarchical generative AI-enabled wireless network digital twin at both the message-level and policy-level, and provide a typical use case with numerical results to validate the effectiveness and efficiency. Finally, open research issues for wireless network digital twins in the 6G era are discussed.

翻译：数字孪生通过同步的数字副本实现对物理实体的仿真、评估与优化，作为一种面向复杂无线网络的前沿技术，正受到越来越多的关注。面向6G，众多创新的无线技术与网络架构为构建无线网络数字孪生带来了新的挑战。为应对这些挑战，人工智能（AI），尤其是蓬勃发展的生成式人工智能，成为一种潜在的解决方案。本文讨论了在6G时代复杂的网络架构、巨大的网络规模、广泛的覆盖范围以及多样化的应用场景下，无线网络数字孪生所需满足的新兴前提条件。我们进一步探讨了生成式人工智能（如Transformer和扩散模型）在物理-数字建模、同步及切片能力等多方面赋能6G数字孪生的应用。随后，我们提出了一个在消息级和策略级均支持生成式人工智能的分层无线网络数字孪生框架，并通过典型用例与数值结果验证了其有效性与效率。最后，本文讨论了6G时代无线网络数字孪生面临的开放研究问题。