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

2026-04-16

AI Summary

This study addresses the challenge of ensuring trustworthiness in out-of-distribution scenarios when performing counterfactual “What-If” analysis with digital twins for 6G networks in edge computing, where disjointed telemetry and validation pipelines hinder reliability. To overcome this limitation, the authors propose a data-driven digital twin framework that leverages cloud-edge协同 scalable telemetry aggregation and semantic alignment to enable performance extrapolation and validation under unseen conditions such as high load. The work introduces mechanism-aware feature engineering and devises a validation mechanism based on symbolic consistency and direction sensitivity, significantly enhancing model reliability in extrapolative settings. Evaluated on a Kubernetes cluster using unified modeling with DNN and XGBoost, the approach achieves regression accuracy with R² > 0.99 for both models, and XGBoost attains a direction accuracy (Sa) exceeding 0.90, effectively supporting proactive resource scaling decisions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Network Digital Twins
• 6G
• What-If Analysis
• Edge Computing
• Trustworthy AI

Innovation

• Network Digital Twin
• Counterfactual What-If Analysis
• Directional Sensitivity
• Regime-Aware Feature Engineering
• Edge Computing Validation