Beyond 5G networks provide solutions for next-generation communications,
especially digital twins networks (DTNs) have gained increasing popularity for
bridging physical space and digital space. However, current DTNs networking
frameworks pose a number of challenges especially when applied in scenarios
that require high communication efficiency and multimodal data processing.
First, current DTNs frameworks are unavoidable regarding high resource
consumption and communication congestion because of original bit-level
communication and high-frequency computation, especially distributed
learning-based DTNs. Second, current machine learning models for DTNs are
domain-specific (e.g. E-health), making it difficult to handle DT scenarios
with multimodal data processing requirements. Last but not least, current
security schemes for DTNs, such as blockchain, introduce additional overheads
that impair the efficiency of DTNs. To address the above challenges, we propose
a large language model (LLM) empowered DTNs networking framework, LLM-Twin.
First, we design the mini-giant model collaboration scheme to achieve efficient
deployment of LLM in DTNs, since LLM are naturally conducive to processing
multimodal data. Then, we design a semantic-level high-efficiency, and secure
communication model for DTNs. The feasibility of LLM-Twin is demonstrated by
numerical experiments and case studies. To our knowledge, this is the first to
propose LLM-based semantic-level digital twin networking framework.

提出了一种大型语言模型（LLM）驱动的数字孪生网络框架（LLM-Twin），通过远程合作方案实现了 LLM 在数字孪生网络中的高效部署，并为数字孪生网络设计了语义级高效和安全的通信模型，经过数值实验和案例研究验证了 LLM-Twin 的可行性，这是第一个提出 LLM 驱动的语义级数字孪生网络框架。