用小模型组件实现跨源跨任务语义通信，提升抗噪能力
Semantic Model Component Implementation for Model-driven Semantic Communications
- 设计可迁移的语义组件模型，支持跨源跨任务部署
- 参数量更小但性能不降，且抗信道噪声能力增强
- 适用于边缘设备轻量化智能升级，如无人车追踪
模型驱动的语义通信核心在于模型的传递。本文设计了语义模型组件（SMC），使智能模型可通过物理信道传输，实现智能在网络中的流动。针对神经网络中通用与个性化参数的特点，提出跨源域、跨任务的语义组件模型。基本模型部署于边缘节点，由大服务器仅通过传输语义组件模型来更新边缘节点，使其能处理不同源和任务。此外，研究了信道噪声对模型性能的影响，提出噪声注入与正则化方法以增强模型抗噪性。实验表明，SMCs 以更少的模型参数实现跨源、跨任务功能，保持性能的同时提升噪声容忍度。最后，基于组件迁移实现了无人车追踪原型系统，验证了模型组件在实际应用中的可行性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。