  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.
