The key feature of model-driven semantic communication is the propagation of the model. The semantic model component (SMC) is designed to drive the intelligent model to transmit in the physical channel, allowing the intelligence to flow through the networks. According to the characteristics of neural networks with common and individual model parameters, this paper designs the cross-source-domain and cross-task semantic component model. Considering that the basic model is deployed on the edge node, the large server node updates the edge node by transmitting only the semantic component model to the edge node so that the edge node can handle different sources and different tasks. In addition, this paper also discusses how channel noise affects the performance of the model and proposes methods of injection noise and regularization to improve the noise resistance of the model. Experiments show that SMCs use smaller model parameters to achieve cross-source, cross-task functionality while maintaining performance and improving the model's tolerance to noise. Finally, a component transfer-based unmanned vehicle tracking prototype was implemented to verify the feasibility of model components in practical applications.

翻译：模型驱动的语义通信的关键特征在于模型的传播。语义模型组件（SMC）旨在驱动智能模型在物理信道中传输，使智能在网络中流动。根据神经网络模型参数具有公共性和个体性的特点，本文设计了跨源域和跨任务的语义组件模型。考虑到基础模型部署在边缘节点，大型服务器节点通过仅向边缘节点传输语义组件模型来更新边缘节点，从而使边缘节点能够处理不同来源和不同任务。此外，本文还讨论了信道噪声如何影响模型性能，并提出了注入噪声和正则化的方法来提高模型的抗噪能力。实验表明，SMC使用更少的模型参数实现了跨源、跨任务的功能，同时保持了性能并提高了模型对噪声的容忍度。最后，实现了一个基于组件迁移的无人车跟踪原型，以验证模型组件在实际应用中的可行性。