基于预训练语言模型的语义重要性感知通信
Semantic Importance-Aware Communications Using Pre-trained Language Models
AI总结：
本文提出语义重要性感知通信方案，利用预训练语言模型量化数据帧语义重要性并进行功率分配，只需引入跨层管理器即可嵌入现有通信系统，实验表明其语义损失低于等优先级通信。
摘要：
本文提出一种使用预训练语言模型（如ChatGPT、BERT等）的语义重要性感知通信（SIAC）方案。具体而言，我们提出一种跨层设计，将预训练语言模型嵌入跨层管理器中或由其连接。该预训练语言模型用于量化数据帧的语义重要性。基于量化得到的语义重要性，我们研究了语义重要性感知的功率分配。与现有基于深度联合信源信道编码（Deep-JSCC）的语义通信方案不同，SIAC只需引入一个跨层管理器即可直接嵌入当前通信系统。实验结果表明，所提出的SIAC方案相比现有的等优先级通信能够实现更低的语义损失。
英文摘要：
This letter proposes a semantic importance-aware communication (SIAC) scheme using pre-trained language models (e.g., ChatGPT, BERT, etc.). Specifically, we propose a cross-layer design with a pre-trained language model embedded in/connected by the cross-layer manager. The pre-trained language model is utilized to quantify the semantic importance of data frames. Based on the quantified semantic importance, we investigate semantic importance-aware power allocation. Unlike existing deep joint source-channel coding (Deep-JSCC)-based semantic communication schemes, SIAC can be directly embedded into current communication systems by only introducing a cross-layer manager. Our experimental results show that the proposed SIAC scheme can achieve lower semantic loss than existing equal-priority communications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
