发表机构
University of Trento; Deggendorf Institute of Technology; Karlstad University（特伦托大学; 代根多夫应用技术大学; 卡尔斯塔德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出PCDT框架，通过预测性认知数字孪生实现6G网络主动资源控制，显著降低分配误差和运营成本，推动DT从被动向主动演进。
AI 中文摘要
未来的6G网络预计将作为智能自主生态系统运行，其中监控、预测和控制被整合到持续的自优化循环中。然而，许多基于数字孪生的网络管理方法仍主要作为网络的同步副本运作。它们观察状态、报告性能下降，并仅在性能风险出现后才触发纠正措施。这导致了认知数字孪生（CDT）的愿景与传统反应式控制回路行为之间的差距。在本文中，我们提出了一个预测性认知数字孪生（PCDT）框架，该框架闭环了从观察到预测认知再到主动资源控制的流程。PCDT维护一个持续的流量世界模型，预测近期负载，并在服务等级协议（SLA）违规发生之前为预测的时间段峰值分配容量。在真实世界流量轨迹上的评估中，PCDT在所有基准基线框架中实现了最低的分配误差，相对于最佳基线（反应式控制），平均绝对误差（MAE）降低了43%，均方根误差（RMSE）降低了32%。相对于阈值启发式（唯一零违规的基线），PCDT将平均分配容量降低了50%，重新配置频率降低了56%，总运营成本降低了50%，表明其性能效率显著更高。这些结果表明，所提出的框架将数字孪生（DT）运行机制从被动表示推进到认知、主动控制和“意图感知”机制，与自主6G网络愿景一致。
英文摘要
Future 6G networks are expected to operate as intelligent and autonomous ecosystems where monitoring, prediction, and control are integrated into continuous self-optimization loops. However, many digital-twin-based network management approaches still act mainly as synchronized replicas of the network. They observe the state, report degradation, and trigger corrective action only after performance risk has appeared. This leaves a gap between the vision of a cognitive digital twin (CDT) and the behavior of conventional reactive control loops. In this paper, we propose a Predictive Cognitive Digital Twin (PCDT) framework that closes the loop from observation to predictive cognition to proactive resource control. PCDT maintains a persistent traffic world model, forecasts near-future load, and allocates capacity for the predicted horizon peak before a Service Level Agreement (SLA) violation occurs. Evaluated on a real-world traffic trace, PCDT achieves the lowest allocation error among all benchmarked baseline frameworks, reducing MAE by 43% and RMSE by 32% relative to the best baseline (reactive control). Relative to the threshold heuristic, the only baseline with zero violations, PCDT reduces mean allocated capacity by 50%, reconfiguration churn by 56%, and total operating cost by 50%, indicating substantially more efficient performance. These results show the proposed framework advances the digital twin (DT) operation mechanism from a passive representation towards a cognitive, proactive control, and "intent-aware" mechanism aligned with autonomous 6G network vision.
CommentsAccepted at IEEE GLOBECOM 2026
