Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler
Zhenyu Tao Wei Xu Yongming Huang Xiaoyun Wang Xiaohu You
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.