Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources

• URL: http://arxiv.org/abs/2604.14787v1
• Date: Thu, 16 Apr 2026 08:49:32 GMT
• Title: Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources
• Abstract summary: Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures.<n>We present a data-driven NDT framework that extends 6G-TWIN with a scalable pipeline for cloud-edge telemetry aggregation.
• Score: 0.5921052011710383
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures, but adoption is often limited by fragmented workflows from telemetry to validation. We present a data-driven NDT framework that extends 6G-TWIN with a scalable pipeline for cloud-edge telemetry aggregation and semantic alignment into unified data models. Our contributions include: (i) scalable cloud-edge telemetry collection, (ii) regime-aware feature engineering capturing the network's scaling behavior, and (iii) a validation methodology based on Sign Agreement and Directional Sensitivity. Evaluated on a Kubernetes-managed cluster, the framework extrapolates performance to unseen high-load regimes. Results show both Deep Neural Network (DNN) and XGBoost achieve high regression accuracy (R2 > 0.99), while the XGBoost model delivers superior directional reliability (Sa > 0.90), making the NDT a trustworthy tool for proactive resource scaling in out-of-distribution scenarios.

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