Semantic Twin Network: Bridging Real-World and Virtual Networks With Semantics
Fengxiao Tang, Linfeng Luo, Zhiqi Guo, Ming Sheng Zhao, Nei Kato · IEEE Wireless Communications · 2025
As an emerging technology, digital twin (DT) enables real-time system monitoring and decision-making by creating digital replicas of real-world entities. However, existing research typically relies on complex mathematical models, rules, and domain-specific knowledge, overlooking the potential of natural language in extracting and modeling the intrinsic features of entities. In this work, we present the novel concept of semantic twin (ST), utilizing semantic modeling to construct semantic replicas of real-world entities, thereby simplifying the representation of complex systems and enhancing their comprehensibility. Building on the idea of ST, we further extend this concept to network communication systems and propose the semantic twin network (STN) framework. STN employs semantic modeling to map both static and dynamic network data into a unified semantic space, leveraging large language models (LLMs) to drive network reasoning and analysis. This novel network architecture simplifies the modeling and analysis of network systems, offering human-understandable insights that empower non-technical personnel. By leveraging pre-trained LLMs, STN facilitates a deeper network understanding and supports efficient, realtime decision-making in complex and evolving network environments. This article comprehensively discusses the design principles, advantages, and integration of STN with existing technologies, highlighting its broad potential applications in dynamic and complex network environments.