AI-Powered Digital Twin for Heterogeneous Wireless Network Simulation
DOI:
https://doi.org/10.22399/ijcesen.3104Keywords:
AI-powered digital twins, Heterogeneous wireless networks (HWNs), Quality of service (QoS), Quality of experience (QoE), Next-generation wireless technologies (5G/6G)Abstract
To address the challenges of managing heterogeneous wireless environments (HWNs), which integrate diverse access networks like 5G, LTE, and Wi-Fi, this research introduces an AI-powered digital twin framework. The framework creates real-time virtual replicas of HWNs to enable dynamic monitoring, simulation, and optimization. Leveraging advanced AI techniques, including reinforcement learning, predictive analytics, and anomaly detection, the framework achieves significant improvements in network performance. Simulations demonstrate a 25% reduction in latency, a 20% improvement in throughput, and a 20% decrease in energy consumption compared to state-of-the-art approaches. Additionally, user-centric Quality of Service (QoS) and Quality of Experience (QoE) models ensure enhanced user satisfaction by tailoring network performance to evolving demands. The authors validated the framework's scalability and adaptability across diverse scenarios, such as dynamic handover management, energy-efficient resource allocation, and fault recovery. These results highlight the potential of the proposed framework to revolutionize network management in next-generation networks, such as 6G. Concluding, this research underscores the framework’s ability to provide real-time optimization, significant energy savings, and improved user satisfaction while addressing challenges like computational overhead. Future work includes integrating renewable energy sources, strengthening cybersecurity features, and expanding capabilities to support emerging technologies like semantic communication.
References
[1] Chai, H., Wang, H., Li, T., & Wang, Z. (2024). Generative ai-driven digital twin for mobile networks. IEEE Network. DOI: https://doi.org/10.1109/MNET.2024.3420702
[2] Sheraz, M., Chuah, T. C., Lee, Y. L., Alam, M. M., & Han, Z. (2024). A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2024.3384272
[3] Mozo, A., Karamchandani, A., Gómez-Canaval, S., Sanz, M., Moreno, J. I., & Pastor, A. (2022). B5GEMINI: AI-driven network digital twin. Sensors, 22(11), 4106. DOI: https://doi.org/10.3390/s22114106
[4] Karamchandani, A., Sanz, M., Burgaleta, Á., De La Cal, L., Mozo, A., Moreno, J. I., ... & López, D. R. (2024, May). Design of an AI-driven Network Digital Twin for advanced 5G-6G network management. In NOMS 2024-2024 IEEE Network Operations and Management Symposium. IEEE. DOI: https://doi.org/10.1109/NOMS59830.2024.10575106
[5] Al-Shareeda, S., Huseynov, K., Cakir, L. V., Thomson, C., Ozdem, M., & Canberk, B. (2024). AI-based traffic analysis in digital twin networks. arXiv preprint arXiv:2411.00681. DOI: https://doi.org/10.1049/PBTE109E_ch4
[6] Duran, K., Shin, H., Duong, T. Q., & Canberk, B. (2025). GenTwin: Generative AI-Powered Digital Twinning for Adaptive Management in IoT Networks. IEEE Transactions on Cognitive Communications and Networking. DOI: https://doi.org/10.1109/TCCN.2025.3527719
[7] Alnaser, A. A., Maxi, M., & Elmousalami, H. (2024). AI-Powered Digital Twins and Internet of Things for Smart Cities and Sustainable Building Environment. Applied Sciences, 14(24), 12056. DOI: https://doi.org/10.3390/app142412056
[8] Chen, J., Yi, C., Okegbile, S. D., Cai, J., & Shen, X. (2023). Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey. IEEE Communications Surveys & Tutorials, 26(1), 706-746. DOI: https://doi.org/10.1109/COMST.2023.3308717
[9] Chen, X., Luo, L., Tang, F., Zhao, M., & Kato, N. (2024). AIGC-based Evolvable Digital Twin Networks: A Road to the Intelligent Metaverse. IEEE Network. DOI: https://doi.org/10.1109/MNET.2024.3411008
[10] Tran-Dang, H., & Kim, D. S. (2025). Digital Twin-empowered intelligent computation offloading for edge computing in the era of 5G and beyond: A state-of-the-art survey. ICT Express. DOI: https://doi.org/10.1016/j.icte.2025.01.002
[11] Bibri, S. E., Huang, J., Jagatheesaperumal, S. K., & Krogstie, J. (2024). The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: a comprehensive systematic review. Environmental Science and Ecotechnology, 100433. DOI: https://doi.org/10.1016/j.ese.2024.100433
[12] Hlophe, M. C., & Maharaj, B. T. (2023). From cyber–physical convergence to digital twins: A review on edge computing use case designs. Applied Sciences, 13(24), 13262. DOI: https://doi.org/10.3390/app132413262
[13] Crespo-Aguado, M., Lozano, R., Hernandez-Gobertti, F., Molner, N., & Gomez-Barquero, D. (2024). Flexible Hyper-Distributed IoT–Edge–Cloud Platform for Real-Time Digital Twin Applications on 6G-Intended Testbeds for Logistics and Industry. Future Internet, 16(11), 431. DOI: https://doi.org/10.3390/fi16110431
[14] Avanzato, R., Beritelli, F., Lombardo, A., & Ricci, C. (2024). Lung-DT: an AI-powered digital twin framework for thoracic health monitoring and diagnosis. Sensors, 24(3), 958. DOI: https://doi.org/10.3390/s24030958
[15] Xu, H., Omitaomu, F., Sabri, S., Li, X., & Song, Y. (2024). Leveraging Generative AI for Smart City Digital Twins: A Survey on the Autonomous Generation of Data, Scenarios, 3D City Models, and Urban Designs. arXiv preprint arXiv:2405.19464. DOI: https://doi.org/10.1007/s44212-024-00060-w
[16] Kumari, S., Thompson, A., & Tiwari, S. (2024). 6G-Enabled Internet of Things-Artificial Intelligence-Based Digital Twins: Cybersecurity and Resilience. In Emerging Technologies and Security in Cloud Computing (pp. 363-394). IGI Global. DOI: https://doi.org/10.4018/979-8-3693-2081-5.ch016
[17] Lin, X., Kundu, L., Dick, C., Obiodu, E., Mostak, T., & Flaxman, M. (2023). 6G digital twin networks: From theory to practice. IEEE Communications Magazine, 61(11), 72-78. DOI: https://doi.org/10.1109/MCOM.001.2200830
[18] Qin, Z., Liang, L., Wang, Z., Jin, S., Tao, X., Tong, W., & Li, G. Y. (2024). AI empowered wireless communications: From bits to semantics. Proceedings of the IEEE. DOI: https://doi.org/10.1109/JPROC.2024.3437730
[19] Oulefki, A., Kheddar, H., Amira, A., Kurugollu, F., & Himeur, Y. Innovative Ai Strategies for Enhancing Smart Building Operations Through Digital Twins: A Survey. Available at SSRN 5015571.
[20] Li, J., Chen, Y., Kimura, T., Wang, T., Wang, R., Kara, D., ... & Abdelzaher, T. (2024, July). Acies-OS: A Content-Centric Platform for Edge AI Twinning and Orchestration. In 2024 33rd International Conference on Computer Communications and Networks (ICCCN) (pp. 1-9). IEEE. DOI: https://doi.org/10.1109/ICCCN61486.2024.10637580
[21] Balasubhramanyam, A., Ramesh, R., Sudheer, R., & Honnavalli, P. B. (2024). Revolutionizing Healthcare: A Review Unveiling the Transformative Power of Digital Twins. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2024.3399744
[22] Khalid, M. (2024). Energy 4.0: AI-enabled digital transformation for sustainable power networks. Computers & Industrial Engineering, 110253. DOI: https://doi.org/10.1016/j.cie.2024.110253
[23] Adibi, S., Rajabifard, A., Shojaei, D., & Wickramasinghe, N. (2024). Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis. Sensors, 24(9), 2793. DOI: https://doi.org/10.3390/s24092793
[24] Bozkaya-Aras, E., Canberk, B., & Schmid, S. (2024). Development of digital twin system for cloud of things. In Cloud of Things (pp. 18-38). Chapman and Hall/CRC. DOI: https://doi.org/10.1201/9781003390954-2
[25] Samuel, P., Saini, A., Poongodi, T., & Nancy, P. (2023). Artificial intelligence–driven digital twins in Industry 4.0. In Digital Twin for Smart Manufacturing (pp. 59-88). Academic Press. DOI: https://doi.org/10.1016/B978-0-323-99205-3.00002-X
[26] AbdlNabi, M. A., Hamza, B. J., & Abdulsadda, A. T. (2024). 6G optical-RF wireless integration: a review on heterogeneous cellular network channel modeling, measurements, and challenges. Telecommunication Systems, 1-44. DOI: https://doi.org/10.1007/s11235-024-01218-2
[27] Sharifi, A., Beris, A. T., Javidi, A. S., Nouri, M. S., Lonbar, A. G., & Ahmadi, M. (2024). Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities. Advanced Engineering Informatics, 61, 102485. DOI: https://doi.org/10.1016/j.aei.2024.102485
[28] Sarker, I. H., Janicke, H., Mohsin, A., Gill, A., & Maglaras, L. (2024). Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects. ICT Express. DOI: https://doi.org/10.1016/j.icte.2024.05.007
[29] Savaglio, C., Barbuto, V., Awan, F. M., Minerva, R., Crespi, N., & Fortino, G. (2023). Opportunistic digital twin: an edge intelligence enabler for smart city. ACM Transactions on Sensor Networks. DOI: https://doi.org/10.1145/3616014
[30] Chen, L., Zheng, S., Wu, Y., Dai, H. N., & Wu, J. (2023). Resource and fairness-aware digital twin service caching and request routing with edge collaboration. IEEE Wireless Communications Letters DOI: https://doi.org/10.1109/LWC.2023.3298200
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
