Semantic-Aware 6G Network Management through Knowledge-Defined Networking

2026-03-13

AI Summary

This work addresses critical limitations in existing semantic communication research, which predominantly focuses on codec design while overlooking semantic correlation decay in multi-hop networks, the absence of semantic awareness in routing strategies, and semantic distortion under dynamic environments. To bridge these gaps, the paper proposes a management-oriented semantic communication framework grounded in Knowledge-Defined Networking (KDN), integrating three core components: knowledge graph–driven semantic reasoning, semantic-aware routing, and adaptive distortion control to achieve end-to-end semantic fidelity and efficient transmission. A novel closed-loop feedback mechanism is introduced, enabling the network layer to proactively preserve semantic validity. Experimental results demonstrate that, compared to baseline approaches, the proposed framework improves semantic delivery success rate by 12%, reduces semantic distortion by 22%, decreases rerouting events by 44%, and enhances throughput efficiency by 14%.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Multiagent Systems: Agent Communication
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

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: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Semantic communication is emerging as a key paradigm for 6G networks, where the goal is not to perfectly reconstruct bits but to preserve the meaning that matters for a given task. This shift can improve bandwidth efficiency, robustness, and application-level performance. However, most existing studies focus solely on encoder-decoder design and ignore network-wide decision-making. As data traverses multiple hops, semantic relevance may decrease, routing may overlook meaningful information, and semantic distortion can increase under dynamic network conditions. To address these challenges, this paper proposes a management-oriented semantic communication framework built upon Knowledge-Defined Networking (KDN). The framework comprises three core modules: a semantic-reasoning module that computes relevance scores by mapping semantic embeddings onto a knowledge graph that encodes task concepts and contextual relationships; a semantic-aware routing mechanism that forwards data along paths that preserve meaning; and a semantic-distortion controller that adaptively adjusts encoding and routing to preserve semantic fidelity. Our ns-3 results show clear benefits: semantic delivery success improves by 12%, semantic distortion decreases by 22%, re-routing events drop by 44%, and throughput efficiency rises by 14% compared to baseline methods (shortest-path, load-based, and distortion-only routing). These results indicate that meaning-aware and feedback-driven control is essential for reliable and scalable semantic communication in future 6G networks.

Problem

• semantic communication
• 6G networks
• network management
• semantic distortion
• multi-hop routing

Innovation

• Semantic Communication
• Knowledge-Defined Networking
• Semantic-Aware Routing
• Knowledge Graph
• Semantic Distortion Control