On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins

2025/09/02 by Zhang, Zifan, Ming Fang, Fang, Minghong +4
Computer Science · Engineering · #Convergence (economics) #Digital Transformation in Industry #FOS: Computer and information sciences #Interoperability #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Resource (disambiguation) #Scalability #Software-Defined Networks and 5G #Task (project management) #Trajectory #Transformation (genetics)

paper · pdf · doi:10.48550/arxiv.2509.02551

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

The integration of digital twinning technologies is driving next-generation networks toward new capabilities, allowing operators to thoroughly understand network conditions, efficiently analyze valuable radio data, and innovate applications through user-friendly, immersive interfaces. Building on this foundation, network digital twins (NDTs) accurately depict the operational processes and attributes of network infrastructures, facilitating predictive management through real-time analysis and measurement. However, constructing precise NDTs poses challenges, such as integrating diverse data sources, mapping necessary attributes from physical networks, and maintaining scalability for various downstream tasks. Unlike previous works that focused on the creation and mapping of NDTs from scratch, we explore intra- and inter-operations among NDTs within a Unified Twin Transformation (UTT) framework, which uncovers a new computing paradigm for efficient transfer, merging, and splitting of NDTs to create task-oriented twins. By leveraging joint multi-modal and distributed mapping mechanisms, UTT optimizes resource utilization and reduces the cost of creating NDTs, while ensuring twin model consistency. A theoretical analysis of the distributed mapping problem is conducted to establish convergence bounds for this multi-modal gated aggregation process. Evaluations on real-world twin-assisted applications, such as trajectory reconstruction, human localization, and sensory data generation, demonstrate the feasibility and effectiveness of interoperability among NDTs for corresponding task development.

Citations

• Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security
• Digital Network Twins for Next-generation Wireless: Creation, Optimization, and Challenges
• A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors
• Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks
• Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks
• Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning
• Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation
• Multimodal Learning with Transformers: A Survey
• Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities
• What Makes Multi-modal Learning Better than Single (Provably)
• Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis
• More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
• Adaptive Federated Optimization
• Convergence Time Optimization for Federated Learning over Wireless Networks
• Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges
• Adaptive Federated Learning in Resource Constrained Edge Computing Systems
• Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Related

• Digital Twin Network: Opportunities and Challenges
• Digital Twin for Non-Terrestrial Networks: Vision, Challenges, and Enabling Technologies
• Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World
• Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning
• Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond