Automatic Generation of Digital Twins for Network Testing

2025-10-03

AI Summary

Manual pre-deployment testing and validation of communication software in autonomous network evolution is time-consuming and labor-intensive. Method: This paper proposes a digital twin (DT) automated generation method aligned with the ITU-T Autonomous Networks architecture, integrating network modeling, automated orchestration, and parameter-driven simulation to generate executable, high-fidelity DT instances directly from real-world network configurations. Contribution/Results: The approach significantly reduces manual configuration overhead and enables seamless integration of the DT environment into existing verification workflows, supporting efficient execution of experimental subsystems. Experimental evaluation demonstrates that the generated DTs meet practical testing requirements in both accuracy and runtime efficiency. To the best of our knowledge, this work achieves the first end-to-end automated construction and closed-loop validation of digital twins compliant with the ITU-T G.1000 series standards.

Technology Category

• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Web Mining and Content Analysis: Web data generation and simulation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The increased use of software in the operation and management of telecommunication networks has moved the industry one step closer to realizing autonomous network operation. One consequence of this shift is the significantly increased need for testing and validation before such software can be deployed. Complementing existing simulation or hardware-based approaches, digital twins present an environment to achieve this testing; however, they require significant time and human effort to configure and execute. This paper explores the automatic generation of digital twins to provide efficient and accurate validation tools, aligned to the ITU-T autonomous network architecture's experimentation subsystem. We present experimental results for an initial use case, demonstrating that the approach is feasible in automatically creating efficient digital twins with sufficient accuracy to be included as part of existing validation pipelines.

Problem

• Automating digital twin creation for network testing
• Reducing manual configuration effort in validation tools
• Enabling efficient autonomous network experimentation subsystems

Innovation

• Automatically generates digital twins for testing
• Aligns with ITU-T autonomous network architecture
• Creates efficient digital twins with sufficient accuracy