Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler (2311.17451v3)
Abstract: 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, 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.
- X. You et al., “Toward 6G TKμTK𝜇\text{TK}\muTK italic_μ extreme connectivity: Architecture, key technologies and experiments,” IEEE Wireless Commun., vol. 30, no. 3, pp. 86–95, June 2023.
- M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems,” Transdisciplinary Perspectives on Complex Systems, pp. 85–113, Aug. 2017.
- M. S. Rodrigo et al., “Digital twins for 5G networks: A modeling and deployment methodology,” IEEE Access, vol. 11, pp. 38 112–38 126, Apr. 2023.
- Z. Zhang et al., “Digital twin-enhanced deep reinforcement learning for resource management in networks slicing,” arXiv preprint arXiv:2311.16876, Nov. 2023. [Online]. Available: https://arxiv.org/abs/2311.16876
- H. Wang et al., “A graph neural network-based digital twin for network slicing management,” IEEE Trans. Ind. Informat., vol. 18, no. 2, pp. 1367–1376, Dec. 2020.
- ITU-R WP5D, “ITU-R framework for IMT-2030,” July 2023. [Online]. Available: https://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt-2030
- Z. Tao et al., “Deep learning-based modeling of 5G core control plane for 5G network digital twin,” IEEE Trans. Cogn. Commun. Netw., early access, Oct. 2023.
- M. Xu et al., “Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services,” arXiv preprint arXiv:2303.16129, Mar. 2023. [Online]. Available: https://arxiv.org/abs/2303.16129
- X. You, “6G extreme connectivity via exploring spatiotemporal exchangeability,” Sci. China Inf. Sci., vol. 66, no. 3, pp. 130 306:1–130 306:3, Feb. 2023.
- C. Ruah, O. Simeone, and B. Al-Hashimi, “A Bayesian framework for digital twin-based control, monitoring, and data collection in wireless systems,” IEEE J. Sel. Areas Commun., vol. 41, no. 10, pp. 3146–3160, Aug. 2023.
- P. Jia and X. Wang, “A new virtual network topology based digital twin for spatial-temporal load-balanced user association in 6G hetnets,” IEEE J. Sel. Areas Commun., vol. 41, no. 10, pp. 3080–3094, Oct. 2023.
- W. Xu et al., “Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,” IEEE J. Sel. Topics Signal Process., vol. 17, no. 1, pp. 9–39, Jan. 2023.
- W. Wu et al., “AI-native network slicing for 6G networks,” IEEE Wireless Commun., vol. 29, no. 1, pp. 96–103, Feb. 2022.
- Z. Qin et al., “Deep learning in physical layer communications,” IEEE Wireless Commun., vol. 26, no. 2, pp. 93–99, Apr. 2019.
- Z. Tao, W. Xu, and X. You, “Digital twin assisted deep reinforcement learning for online admission control in sliced network,” arXiv preprint arXiv:2310.09299, Oct. 2023. [Online]. Available: https://arxiv.org/abs/2310.09299
- Zhenyu Tao (6 papers)
- Wei Xu (535 papers)
- Yongming Huang (98 papers)
- Xiaoyun Wang (21 papers)
- Xiaohu You (177 papers)