Trust-Aware Adaptive Network Digital Twin for Multi-Slice Orchestration in 6G NTN-Edge Networks
Abstract
Although Network Digital Twins are considered enablers for sixth generation (6G) networks, there are several challenges involved in reliably and repeatedly leveraging NDTs for various use cases. In this work, we propose a trust-aware and adaptive framework called SATE- NDT, for multi-slice orchestration across various network domains (e.g. edge and non-terrestrial networks). The framework incorporates misalignment telemetry, semantic alignment, and drift compensation. Further, the framework integrates prediction and resource planning, as well as a trust gate. Compared to the state-of-the-art, SATE-NDT provides improvements in control and synchronization of various service classes (i.e., eMBB, URLLC and mMTC) in 6G networks. Rather than following a conventional control structure where the control and/or estimation errors are compensated, SATE-NDT aims to minimize eMBB, URLLC, and mMTC service class latencies and violation rates, by adjusting the synchronization frequency based on recent forecast errors and directional agreement. Further, SATE-NDT achieves improvement in eMBB and mMTC service class SLAs, while increasing the URLLC class violation rate. The results suggest trustworthy 6G NDT orchestration requires consideration of synchronization costs, model directionality, and slice-specific risks. Reliance on prediction is not sufficient.
Keywords
6G; network digital twin; AI-native networks; network slicing; edge computing
References
AbdulRahman, S., Otoum, S., Bouachir, O., & Mourad, A. (2023). Management of digital twin- driven IoT using federated learning. IEEE Journal on Selected Areas in Communications, 41(11), 3636–3649. https://doi.org/10.1109/JSAC.2023.3310102 Ali, A., & Arslan, H. (2025). AI-driven digital twins: Optimizing 5G/6G network slicing with NTNs [Preprint]. arXiv. https://arxiv.org/abs/2505.08328 Dao, N.-N., Tu, N. H., Hoang, T.-D., Nguyen, T.-H., Nguyen, L. V., Lee, K., Park, L., Na, W., & Cho, S. (2024). A review on new technologies in 3GPP standards for 5G access and beyond. Computer Networks, 245, 110370. https://doi.org/10.1016/j.comnet.2024.110370 European Telecommunications Standards Institute. (2024). Zero-touch network and service management ; Network digital twin (ETSI GR ZSM 015 V1.1.1). https://www.etsi.org/deliver/etsi_gr/ZSM/001_099/015/01.01.01_60/gr_ZSM015v01010 1p.pdf Faye, S., Camelo Botero, M., Sottet, J.-S., Sommer, C., Franke, M., Baudouin, J., Castellanos, G., Decorme, R., Fanti, M. P., Fuladi, R., Kesik, G., Mendes, B., Murphy, C., Parker, S., Pryor, S., Senouci, S. M., & Turcanu, I. (2024). Integrating network digital twinning into future AI-based 6G systems: The 6G-TWIN vision. In 2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) (pp. 883–888). IEEE. https://doi.org/10.1109/EuCNC/6GSummit60053.2024.10597058 Faye, S., Soto, P., Volpe, G., Zaki-Hindi, A., Hensel, B., Castellanos, G., Turcanu, I., Sommer, C., Senouci, S. M., Belogaev, A., & Camelo Botero, M. (2025). A functional framework for network digital twins. In 2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) (pp. 1–6). IEEE. https://doi.org/10.1109/EuCNC/6GSummit63408.2025.11036908 Ferriol-Galmés, M., Suárez-Varela, J., Paillissé, J., Shi, X., Xiao, S., Cheng, X., Barlet-Ros, P., & Cabellos-Aparicio, A. (2022). Building a digital twin for network optimization using graph neural networks. Computer Networks, 217, 109329. https://doi.org/10.1016/j.comnet.2022.109329 Giordani, M., & Zorzi, M. (2021). Non-terrestrial networks in the 6G era: Challenges and opportunities. IEEE Network, 35(2), 244–251. https://doi.org/10.1109/MNET.011.2000493 Gomes, R. L., Bittencourt, L. F., & Madeira, E. R. M. (2020). Reliability-aware network slicing in elastic demand scenarios. IEEE Communications Magazine, 58(10), 29–34. https://doi.org/10.1109/MCOM.001.2000753 Hamdi, W., Ksouri, C., Bulut, H., & Mosbah, M. (2024). Network slicing-based learning techniques for IoV in 5G and beyond networks. IEEE Communications Surveys & Tutorials, 26(3), 1989–2047. https://doi.org/10.1109/COMST.2024.3372083 He, S., Dong, H., Wang, L., & An, Z. (2025). A real-time data collection approach for 6G AI- native networks [Preprint]. arXiv. https://arxiv.org/abs/2509.01276 Hurtado Sánchez, J. A., Casilimas, K., & Caicedo Rendon, O. M. (2022). Deep reinforcement learning for resource management on network slicing: A survey. Sensors, 22(8), 3031. https://doi.org/10.3390/s22083031 International Telecommunication Union. (2022). Digital twin network—Requirements and architecture (Recommendation ITU-T Y.3090). https://www.itu.int/rec/T-REC-Y.3090- 202202-I/en Jimenez Agudelo, J., Soto, P., Zaki-Hindi, A., Sottet, J.-S., Faye, S., Slamnik-Kriještorac, N., Marquez-Barja, J., & Camelo Botero, M. (2026). Towards trustworthy 6G network digital twins: A framework for validating counterfactual what-if analysis in edge computing resources [Preprint]. arXiv. https://arxiv.org/abs/2604.14787 Khan, L. U., Saad, W., Niyato, D., Han, Z., & Hong, C. S. (2022). Digital-twin-enabled 6G: Vision, architectural trends, and future directions. IEEE Communications Magazine, 60(1), 74–80. https://doi.org/10.1109/MCOM.001.21143 Koo, J., Mendiratta, V. B., Rahman, M. R., & Walid, A. (2019). Deep reinforcement learning for network slicing with heterogeneous resource requirements and time varying traffic dynamics. In 2019 15th International Conference on Network and Service Management (pp. 1–5). IEEE. https://doi.org/10.23919/CNSM46954.2019.9012702 Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R. V., Venkataraman, H., Trestian, R., & Nguyen, H. X. (2022). Digital twins: A survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys & Tutorials, 24(4), 2255–2291. https://doi.org/10.1109/COMST.2022.3208773 Peng, H., & Shen, X. (2021). Multi-agent reinforcement learning based resource management in MEC- and UAV-assisted vehicular networks. IEEE Journal on Selected Areas in Communications, 39(1), 131–141. Sanjalawe, Y., Fraihat, S., Al-E’mari, S., & Makhadmeh, S. N. (2026). Bridging the gap: A comprehensive survey on AI-driven digital twin networks for future wireless systems. Journal of King Saud University Computer and Information Sciences, 38, Article 113. https://doi.org/10.1007/s44443-026-00522-y Saqib, N. U., Song, S., Xie, H., Cao, Z., Hahm, G.-J., Cheon, K.-Y., Kwon, H., Park, S., Jeon, S.-W., & Jin, H. (2024). Digital twin enabled cellular network management and prediction. ICT Express, 10(3), 479–484. https://doi.org/10.1016/j.icte.2024.02.011 Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x Shen, X., Gao, J., Wu, W., Li, M., Zhou, C., & Zhuang, W. (2022). Holistic network virtualization and pervasive network intelligence for 6G. IEEE Communications Surveys & Tutorials, 24(1), 1–30. https://doi.org/10.1109/COMST.2021.3135829 Sheraz, M., Chuah, T. C., Lee, Y. L., Alam, M. M., Al-Habashna, A., & Han, Z. (2024). A comprehensive survey on revolutionizing connectivity through artificial intelligence- enabled digital twin network in 6G. IEEE Access, 12, 49184–49215. https://doi.org/10.1109/ACCESS.2024.3384272 Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., & Wang, A. (2022). Network telemetry framework (RFC 9232). Internet Engineering Task Force. https://www.rfc- editor.org/rfc/rfc9232.html Wu, W., Huang, X., & Luan, T. H. (2024). AI-native network digital twin for intelligent network management in 6G [Preprint]. arXiv. https://arxiv.org/abs/2410.01584 Zaki-Hindi, A., Sottet, J.-S., Turcanu, I., & Faye, S. (2024). Network digital twins for 6G: Defining data taxonomy and data models. In 2024 IEEE Conference on Standards for Communications and Networking (pp. 124–128). IEEE. https://doi.org/10.1109/CSCN63874.2024.10849705 Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu, Q., Boucadair, M., & Jacquenet, C. (2024). Network digital twin: Concepts and reference architecture (Internet-Draft draft- irtf-nmrg-network-digital-twin-arch-07). Internet Engineering Task Force. https://datatracker.ietf.org/doc/draft-irtf-nmrg-network-digital-twin-arch/07/
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