构建可验证的6G网络数字孪生，实现边缘资源预判与可信决策。
Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources
- 通过云边数据聚合与语义对齐，建立统一数字孪生模型。
- XGBoost模型在未见高负载下方向可靠性超0.90，回归准确率超0.99。
- 适合需要前瞻资源调度的6G边缘系统开发者与运维人员。
网络数字孪生（NDTs）为6G云边架构提供安全的“假设分析”能力，但其应用常受限于从遥测到验证的流程碎片化。本文提出一种数据驱动的NDT框架，扩展6G-TWIN，构建可扩展的云边遥测采集与语义对齐流水线。贡献包括：(i) 可扩展的云边遥测收集；(ii) 捕捉网络缩放行为的感知特征工程；(iii) 基于符号一致性与方向敏感性的验证方法。在Kubernetes管理的集群上评估，该框架可外推至未见过的高负载场景。结果表明，深度神经网络（DNN）与XGBoost均达到高回归精度（R² > 0.99），其中XGBoost模型方向可靠性（Sa > 0.90）更优，使数字孪生成为分布外场景下主动资源扩缩的可信工具。
原文摘要 · Abstract (English)
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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