PCDT: A Predictive Cognitive Digital Twin Framework for Intelligent and Autonomous 6G Network Ecosystems

2026-10-07

AI Summary

This study addresses the limitations of 6G digital twins that passively synchronize network states and trigger control actions only after service-level agreement (SLA) violations occur. To overcome these issues, this work proposes a predictive cognitive digital twin framework. By constructing a persistent traffic world model and employing near-future workload forecasting techniques, the proposed approach upgrades digital twins from passive representations to intent-aware proactive control mechanisms, bridging the gap between observation and prediction to enable a closed-loop proactive capacity allocation based on predicted traffic peaks. Experimental results demonstrate that the mean absolute error (MAE) and root mean square error (RMSE) of predictions are reduced by 43% and 32%, respectively. Furthermore, compared with a zero-violation baseline, the average allocated capacity, reconfiguration frequency, and operational costs are all decreased by 50%.

Abstract

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.

Problem

• 6G networks
• cognitive digital twin
• predictive control
• network management
• autonomous ecosystems

Innovation

• Predictive Cognitive Digital Twin
• Traffic World Model
• Proactive Resource Control
• Intent-Aware
• 6G Networks