Digital twin, which enables emulation, evaluation, and optimization of physical entities through synchronized digital replicas, has gained increasingly 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 implementation, physical-digital 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：数字孪生通过同步数字副本实现对物理实体的仿真、评估与优化，作为复杂无线网络的一项前景广阔的技术日益受到关注。面向6G，众多创新型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AI，展现出作为潜在解决方案的潜力。本文针对6G时代复杂的网络架构、庞大的网络规模、广泛的覆盖范围以及多样化的应用场景，探讨了无线网络数字孪生面临的新需求。我们进一步探索了生成式AI（如Transformer和扩散模型）在实现、物理-数字同步及切面能力等多维度赋能6G数字孪生的应用。随后，我们提出了一种分层式生成式AI赋能的无线网络数字孪生框架，涵盖消息级与策略级两个层面，并通过典型用例与数值结果验证了其有效性与高效性。最后，讨论了6G时代无线网络数字孪生的开放性研究问题。