Knowledge-Defined and Twin-Assisted Network Management for 6G

2025-09-27

AI Summary

To address the autonomous management challenges posed by the high complexity, strong dynamics, and heterogeneity of 6G networks, this paper proposes a knowledge-defined and digital twin–driven autonomous management architecture. The architecture introduces a novel modular knowledge definition framework that integrates real-time digital twin modeling, graph neural network–based semantic reasoning, and zero-shot learning—enabling policy matching and intent response for previously unseen scenarios without model retraining. A closed-loop perception–reasoning–decision mechanism supports semantic-level intent understanding and real-time adaptive optimization. Simulation results demonstrate that, compared to baseline approaches, the proposed solution reduces policy response latency by 42%, improves SLA compliance rate by 31%, and increases intent matching accuracy by 28%, significantly enhancing system generalizability, real-time responsiveness, and robustness.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

The increasing complexity, dynamism, and heterogeneity of 6G networks demand management systems that can reason proactively and generalize beyond pre-defined cases. In this paper, we propose a modular, knowledge-defined architecture that integrates Digital Twin models with semantic reasoning and zero-shot learning to enable autonomous decision-making for previously unseen network scenarios. Real-time data streams are used to maintain synchronized virtual replicas of the physical network, which also forecast short-term state transitions. These predictions feed into a knowledge plane that constructs and updates a graph-based abstraction of the network, enabling context-aware intent generation via graph neural reasoning. To ensure adaptability without retraining, the management plane performs zero-shot policy matching by semantically embedding candidate intents and selecting suitable pre-learned actions. The selected decisions are translated and enforced through the control plane, while a closed-loop feedback mechanism continuously refines predictions, knowledge, and policies over time. Simulation results confirm that the proposed framework observes notable improvements in policy response time, SLA compliance rate, and intent matching accuracy.

Problem

• Managing 6G network complexity and dynamism autonomously
• Enabling decision-making for unseen network scenarios
• Improving policy response time and SLA compliance

Innovation

• Digital Twin models enable real-time network state synchronization
• Graph neural reasoning constructs context-aware network abstractions
• Zero-shot learning selects policies without retraining requirements