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Vol. 13 No. 03 (2026)
Volume 13 Issue 03

Articles

Integrated Multimodal Sensing and Edge-Enabled Digital Twins for 6G Networks: A Framework for Cross-Domain Standardization and Beam Management

Published 2026-03-06

  • Dr. Patricia Vance

Dr. Patricia Vance
Department of Computer Science and Engineering, ETH Zürich, Switzerland

Abstract

The transition from fifth-generation (5G) to sixth-generation (6G) wireless systems necessitates a paradigm shift in how networks perceive and interact with the physical environment. This research explores the integration of multimodal sensing, Digital Twins (DT), and Edge/Fog computing as the foundational pillars of 6G communication. By synthesizing data from diverse sensors-including cameras, LiDAR, and radar-the network can construct high-fidelity digital replicas of the physical world. This study investigates the "DeepSense 6G" and "Deepverse 6G" frameworks for multimodal data management and examines the viability of training machine learning models in digital environments for real-world deployment. A primary focus is placed on the role of Edge Computing in reducing latency and enhancing privacy in IoT-based smart cities. Furthermore, we analyze the critical need for cross-domain standardization to enable secure, real-time digital twin deployments. Our findings suggest that sensing-aided beam prediction and edge-based intelligence can significantly mitigate the overhead of massive MIMO systems, provided that robust architectures for data management and privacy-preserving protocols are implemented. The article concludes with a comprehensive roadmap for addressing the research gaps in standardization and integrated sensing and communication (ISAC).

Keywords

6G Wireless, Digital Twins, Edge Computing, Multimodal Sensing

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References

  1. Abdellatif, A. A., Mohamed, A., Chiasserini, C. F., Tlili, M. and Erbad, A. Edge Computing for Smart Health: Context-Aware Approaches, Opportunities, and Challenges. IEEE Network, vol. 33, no. 3, pp. 196 - 203, May/June 2019.
  2. AlAwlaqi, L., AlDawod, A., AlFowzan, R. and AlBraheem, L. The Requirements of Fog/Edge Computing-Based IoT Architecture. 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 2021, pp. 0051 - 0057.
  3. Alkhateeb, A. DeepSense 6G: A Large-Scale Real-World Multimodal Sensing and Communication Dataset. IEEE Commun. Mag., 2023.
  4. Debauche, O., Mahmoudi, S., Guttadauria, A. A New Edge Computing Architecture for IoT and Multimedia Data Management. Information, 2022.
  5. Demirhan, U. Deepverse 6G: A Framework for Synthetic Multi-Modal Sensing and Communication Datasets. arXiv preprint, 2023.
  6. Demirhan, U. and Alkhateeb, A. Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles. IEEE Commun. Mag., 2022.
  7. Gheisari, M., Pham, Q. -V., Alazab, M., Zhang, X., Fernández-Campusano, C. and Srivastava, G. ECA: An Edge Computing Architecture for Privacy-Preserving in IoT-Based Smart City. IEEE Access, vol. 7, pp. 155779 - 155786, 2019.
  8. Intharawijitr, K., Iida, K., Koga, H. and Yamaoka, K. Practical Enhancement and Evaluation of a Low-Latency Network Model Using Mobile Edge Computing. 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), Turin, Italy, 2017, pp. 567 - 574.
  9. Jiang, S. and Alkhateeb, A. Sensing Aided OTFS Channel Estimation for Massive MIMO Systems. arXiv preprint arXiv: 2209.11321, 2022.
  10. Jiang, S. and Alkhateeb, A. Digital Twin Based Beam Prediction: Can We Train in the Digital World and Deploy in Reality? arXiv preprint, 2023.
  11. Omoniwa, B., Hussain, R., Javed, M. A., Bouk, S. H. and Malik, S. A. Fog/Edge Computing-Based IoT (FECIoT): Architecture, Applications, and Research Issues. IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4118 - 4149, June 2019.
  12. Spiceworks. What is Edge Computing? Available at: https://www.spiceworks.com/tech/edge-computing/articles/what-is-edge-computing/
  13. S. R. Varanasi, S. S. S. Valiveti, M. Adnan, M. I. Faruk, M. J. Hossain and M. M. T. G. Manik, "Cross-Domain Standardization and Secure Edge Intelligence for Real-Time Digital Twin Deployments in Next-Generation Communication Systems," in IEEE Communications Standards Magazine, doi: 10.1109/MCOMSTD.2026.3662187.
  14. Xue, H., Huang, B., Qin, M., Zhou, H. and Yang, H. Edge Computing for Internet of Things: A Survey. 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom), Rhodes, Greece, 2020, pp. 755 - 760.

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