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。

LLM-Twin: 基于 Mini-Giant 模型推动的超越 5G 的数字孪生网络框架与安全语义通信和计算

LLM-Twin: Mini-Giant Model-driven Beyond 5G Digital Twin Networking  Framework with Semantic Secure Communication and Computation

In future 6G Mobile Edge Computing (MEC), autopilot systems require the
capability of processing multimodal data with strong interdependencies.
However, traditional heuristic algorithms are inadequate for real-time
scheduling due to their requirement for multiple iterations to derive the
optimal scheme. We propose a novel TSNet-SAC based on Transformer, that
utilizes heuristic algorithms solely to guide the training of TSNet.
Additionally, a Sliding Augment Component (SAC) is introduced to enhance the
robustness and resolve algorithm defects. Furthermore, the Extender component
is designed to handle multi-scale training data and provide network
scalability, enabling TSNet to adapt to different access scenarios. Simulation
demonstrates that TSNet-SAC outperforms existing networks in accuracy and
robustness, achieving superior scheduling-making latency compared to heuristic
algorithms.

提出了一种基于 Transformer 网络的 TSNet-SAC 方案，用于 6G 移动边缘计算机自动驾驶系统中的实时调度和多模数据处理，通过引入幻数算法来指导 TSNet 的训练，以及增强鲁棒性和解决算法缺陷的滑动增强组件（SAC），并设计了 Extender 组件来处理多尺度训练数据和提供网络可扩展性，从而在准确性和鲁棒性上优于现有网络，并实现了与启发式算法相比更优秀的调度决策延迟。