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Digital twin, which enables emulation, evaluation, and optimization ofphysical entities through synchronized digital replicas, has gainedincreasingly attention as a promising technology for intricate wirelessnetworks. For 6G, numerous innovative wireless technologies and networkarchitectures have posed new challenges in establishing wireless networkdigital twins. To tackle these challenges, artificial intelligence (AI),particularly the flourishing generative AI, emerges as a potential solution. Inthis article, we discuss emerging prerequisites for wireless network digitaltwins considering the complicated network architecture, tremendous networkscale, extensive coverage, and diversified application scenarios in the 6G era.We further explore the applications of generative AI, such as transformer anddiffusion model, to empower the 6G digital twin from multiple perspectivesincluding implementation, physical-digital synchronization, and slicingcapability. Subsequently, we propose a hierarchical generative AI-enabledwireless network digital twin at both the message-level and policy-level, andprovide a typical use case with numerical results to validate the effectivenessand efficiency. Finally, open research issues for wireless network digitaltwins in the 6G era are discussed.

Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler