A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI) technologies to enable many connected intelligence services. In order to handle the large amounts of network data based on digital twins (DTs), wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints by utilizing AI techniques such as causal reasoning. In this paper, a novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems. The CSC system is posed as an imitation learning (IL) problem, where the transmitter, with access to optimal network control policies using a DT, teaches the receiver using SC over a bandwidth limited wireless channel how to improve its knowledge to perform optimal control actions. The causal structure in the source data is extracted using novel approaches from the framework of deep end-to-end causal inference, thereby enabling the creation of a semantic representation that is causally invariant, which in turn helps generalize the learned knowledge of the system to unseen scenarios. The CSC decoder at the receiver is designed to extract and estimate semantic information while ensuring high semantic reliability. The receiver control policies, semantic decoder, and causal inference are formulated as a bi-level optimization problem within a variational inference framework. This problem is solved using a novel concept called network state models, inspired from world models in generative AI, that faithfully represents the environment dynamics leading to data generation. Simulation results demonstrate that the proposed CSC system outperforms state-of-the-art SC systems by achieving better semantic reliability and reduced semantic representation.

翻译：数字孪生（DT）通过构建物理世界的虚拟表征，结合通信（如6G）、计算（如边缘计算）及人工智能（AI）技术，赋能众多互联智能服务。为处理基于数字孪生的大规模网络数据，无线系统可借助语义通信（SC）范式，在严格通信约束下利用因果推理等AI技术辅助决策。本文提出了一种面向DT无线系统的因果语义通信（CSC）框架。将CSC系统建模为模仿学习（IL）问题：发射端利用DT获取最优网络控制策略，通过带宽受限的无线信道以SC方式教导接收端提升知识水平，从而执行最优控制动作。通过深度端到端因果推理框架中的创新方法提取源数据的因果结构，构建具有因果不变性的语义表征，进而使系统习得的知识可泛化至未见场景。接收端CSC解码器设计兼顾语义信息提取与语义可靠性保障。将接收端控制策略、语义解码器及因果推理建模为变分推理框架下的双层优化问题，并利用受生成式AI世界模型启发提出的网络状态模型求解——该模型忠实表征生成数据的环境动力学特征。仿真结果表明，所提CSC系统在提升语义可靠性的同时降低了语义表征规模，性能优于现有顶尖SC系统。