When Digital Twin Meets Generative AI: Intelligent Closed-Loop Network Management (2404.03025v2)
Abstract: Generative artificial intelligence (GAI) and digital twin (DT) are advanced data processing and virtualization technologies to revolutionize communication networks. Thanks to the powerful data processing capabilities of GAI, integrating it into DT is a potential approach to construct an intelligent holistic virtualized network for better network management performance. To this end, we propose a GAI-driven DT (GDT) network architecture to enable intelligent closed-loop network management. In the architecture, various GAI models can empower DT status emulation, feature abstraction, and network decision-making. The interaction between GAI-based and model-based data processing can facilitate intelligent external and internal closed-loop network management. To further enhance network management performance, three potential approaches are proposed, i.e., model light-weighting, adaptive model selection, and data-model-driven network management. We present a case study pertaining to data-model-driven network management for the GDT network, followed by some open research issues.
- E. Glaessgen and D. Stargel, “The digital twin paradigm for future NASA and U.S. air force vehicles,” in Proc. Struct. Dyn. Mater. Conf. Special Session: Digital Twin, Honolulu, HI, USA, Apr. 2012, pp. 1–14.
- L. Hui, M. Wang, L. Zhang, L. Lu, and Y. Cui, “Digital twin for networking: A data-driven performance modeling perspective,” IEEE Netw., vol. 37, no. 3, pp. 202–209, 2023.
- X. Shen, J. Gao, W. Wu, M. Li, C. Zhou, and W. Zhuang, “Holistic network virtualization and pervasive network intelligence for 6G,” IEEE Commun. Surveys Tuts., vol. 24, no. 1, pp. 1–30, Firstquarter 2022.
- M. Xu, H. Du, D. Niyato, J. Kang, Z. Xiong, S. Mao, Z. Han, A. Jamalipour, D. I. Kim, X. Shen, V. C. M. Leung, and H. V. Poor, “Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services,” IEEE Commun. Surv. Tut., pp. 1–45, Jan. 2024, Early Access.
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Commun. ACM, vol. 63, no. 11, pp. 139–144, Oct. 2020.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” vol. 30, Long Beach, CA, USA, Dec. 2017, pp. 5998–6008.
- J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” vol. 33, Virtual, Dec. 2020, pp. 6840–6851.
- L. He, Z. Wang, L. Wang, and F. Li, “Multimodal mutual attention-based sentiment analysis framework adapted to complicated contexts,” IEEE Trans. Circuits Syst. Video Technol., vol. 33, no. 12, pp. 7131–7143, May 2023.
- D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proc. Int. Conf. Learn. Represent. (ICLR), Banff, AB, Canada, Apr. 2014, pp. 1–14.
- X. Huang, H. Yang, S. Hu, and X. Shen, “Digital twin-driven network architecture for video streaming,” IEEE Netw., pp. 1–8, Apr. 2024, Early Access.
- R. Bhattacharyya, B. Wulfe, D. J. Phillips, A. Kuefler, J. Morton, R. Senanayake, and M. J. Kochenderfer, “Modeling human driving behavior through generative adversarial imitation learning,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 3, pp. 2874–2887, Dec. 2022.
- A. Ajay, Y. Du, A. Gupta, J. Tenenbaum, T. Jaakkola, and P. Agrawal, “Is conditional generative modeling all you need for decision-making?” in Proc. Int. Conf. Learn. Represent. (ICLR), Kigali, Rwanda, May 2023, pp. 1–24.
- W. Wu, M. Li, K. Qu, C. Zhou, X. Shen, W. Zhuang, X. Li, and W. Shi, “Split learning over wireless networks: Parallel design and resource management,” IEEE J. Sel. Areas Commun., vol. 41, no. 4, pp. 1051–1066, Feb. 2023.
- W. Park, D. Kim, Y. Lu, and M. Cho, “Relational knowledge distillation,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Long Beach, CA, USA, Jun. 2019, pp. 3967–3976.
- X. Huang, S. Hu, H. Yang, X. Wang, Y. Pei, and X. Shen, “Digital twin-based network management for better QoE in multicast short video streaming,” arXiv preprint arXiv:2401.12826, Jan. 2024.