demonstration

Demo: Scalable Digital Twin System for Mobile Networks with Generative AI

Authors:

Tong Li,

Yong LiAuthors Info & Claims

MobiSys '23: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services

Pages 610 - 611

https://doi.org/10.1145/3581791.3597297

Published: 18 June 2023 Publication History

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Abstract

Digital Twin brings a new realization approach to the modeling of mobile networks. Mobile networks, as complex systems comprising multiple components, such as mobile users, base stations, and wireless environments, have intricate interactions and relationships with each other. By creating a virtual replica of each physical mobile network entity in a virtual space, we build a scalable digital twin system for mobile networks with generative AI. The system can interact with multiple optimizers to evaluate and display real-time simulation results. A companion video can be accessed using the link below. https://youtu.be/xtcBIXPzvkc

References

[1]

Shuodi Hui, Huandong Wang, Zhenhua Wang, Xinghao Yang, Zhongjin Liu, Depeng Jin, and Yong Li. 2022. Knowledge Enhanced GAN for IoT Traffic Generation. In Proceedings of the ACM Web Conference 2022. 3336--3346.

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[2]

Yuan Yuan, Jingtao Ding, Huandong Wang, Depeng Jin, and Yong Li. 2022. Activity Trajectory Generation via Modeling Spatiotemporal Dynamics. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4752--4762.

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[3]

Guozhen Zhang, Zihan Yu, Depeng Jin, and Yong Li. 2022. Physics-infused machine learning for crowd simulation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2439--2449.

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[4]

Jun Zhang, Depeng Jin, and Yong Li. 2022. Mirage: an efficient and extensible city simulation framework (systems paper). Proceedings of the 30th International Conference on Advances in Geographic Information Systems (2022).

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Cited By

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Li TLong QChai HZhang SJiang FLiu HHuang WJin DLi Y(2025)Generative AI Empowered Network Digital Twins: Architecture, Technologies, and ApplicationsACM Computing Surveys10.1145/371168257:6(1-43)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3711682
Zhang XXue K(2025)Hybrid Intelligent Optimization Model for Animation Design in Virtual Reality and Augmented RealityJournal of Circuits, Systems and Computers10.1142/S0218126625501531Online publication date: 6-Feb-2025
https://doi.org/10.1142/S0218126625501531
Pitchandi PSadu VKalaipoonguzhali VArivukarasi M(2025)A novel video anomaly detection using hybrid sand cat Swarm optimization with backpropagation neural network by UCSD Ped 1 datasetJournal of Visual Communication and Image Representation10.1016/j.jvcir.2025.104414(104414)Online publication date: Feb-2025
https://doi.org/10.1016/j.jvcir.2025.104414
Show More Cited By

Index Terms

Demo: Scalable Digital Twin System for Mobile Networks with Generative AI
1. Computing methodologies
  1. Modeling and simulation
2. Networks
  1. Network performance evaluation
    1. Network simulations

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Published In

MobiSys '23: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services

June 2023

651 pages

ISBN:9798400701108

DOI:10.1145/3581791

General Chairs:
Petteri Nurmi,
Pan Hui,
Program Chairs:
Ardalan Amiri Sani,
Yunxin Liu

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2023

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Demonstration

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MobiSys '23

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SIGMOBILE

MobiSys '23: 21st Annual International Conference on Mobile Systems, Applications and Services

June 18 - 22, 2023

Helsinki, Finland

Acceptance Rates

MobiSys '23 Paper Acceptance Rate 41 of 198 submissions, 21%;

Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Li TLong QChai HZhang SJiang FLiu HHuang WJin DLi Y(2025)Generative AI Empowered Network Digital Twins: Architecture, Technologies, and ApplicationsACM Computing Surveys10.1145/371168257:6(1-43)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3711682
Zhang XXue K(2025)Hybrid Intelligent Optimization Model for Animation Design in Virtual Reality and Augmented RealityJournal of Circuits, Systems and Computers10.1142/S0218126625501531Online publication date: 6-Feb-2025
https://doi.org/10.1142/S0218126625501531
Pitchandi PSadu VKalaipoonguzhali VArivukarasi M(2025)A novel video anomaly detection using hybrid sand cat Swarm optimization with backpropagation neural network by UCSD Ped 1 datasetJournal of Visual Communication and Image Representation10.1016/j.jvcir.2025.104414(104414)Online publication date: Feb-2025
https://doi.org/10.1016/j.jvcir.2025.104414
Harikumar VBijudas C(2025)Digital Twin for Health Monitoring of a Cantilever Beam Using Support Vector MachineJournal of Vibration Engineering & Technologies10.1007/s42417-024-01608-513:1Online publication date: 9-Jan-2025
https://doi.org/10.1007/s42417-024-01608-5
Sai SKumar SGaur AGoyal SChamola VHussain A(2025)Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable FarmingCognitive Computation10.1007/s12559-025-10420-617:1Online publication date: 4-Feb-2025
https://doi.org/10.1007/s12559-025-10420-6
Mathew DEbem DIkegwu AUkeoma PDibiaezue N(2025)Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for HumanNeural Processing Letters10.1007/s11063-025-11732-257:1Online publication date: 7-Feb-2025
https://doi.org/10.1007/s11063-025-11732-2
Kellil MBen Hadj Said SThi MJanneteau COlivereau A(2024)Addressing the Scalability of Network Digital Twins: A Network Sampling Approach2024 20th International Conference on Network and Service Management (CNSM)10.23919/CNSM62983.2024.10814373(1-7)Online publication date: 28-Oct-2024
https://doi.org/10.23919/CNSM62983.2024.10814373
Liu HSu WLi THuang WLi Y(2024)Digital Twin Enhanced Multi-Agent Reinforcement Learning for Large-Scale Mobile Network Coverage OptimizationACM Transactions on Knowledge Discovery from Data10.1145/370264419:1(1-23)Online publication date: 31-Oct-2024
https://dl.acm.org/doi/10.1145/3702644
Gong JLi TWang HLiu YWang XWang ZDeng CFeng JJin DLi Y(2024)KGDA: A Knowledge Graph Driven Decomposition Approach for Cellular Traffic PredictionACM Transactions on Intelligent Systems and Technology10.1145/369065015:6(1-22)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3690650
Ma YLi TDu YDustdar SWang ZLi YGopalakrishnan VWang JAyyub Qazi ITyson G(2024)Sustainable Connections: Exploring Energy Efficiency in 5G NetworksProceedings of the 20th International Conference on emerging Networking EXperiments and Technologies10.1145/3680121.3697806(33-40)Online publication date: 9-Dec-2024
https://dl.acm.org/doi/10.1145/3680121.3697806
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Recommendations

Generative AI Empowered Network Digital Twins: Architecture, Technologies, and Applications

Manufacturing Digital Twin Standards

DEMO: Starving Permission-Hungry Android Apps Using SecuRank

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