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Semantic Importance-Aware Communications Using Pre-Trained Language Models
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DOI：10.1109/LCOMM.2023.3293805.png)
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摘要
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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.
Keyword:
Semantics
Chatbots
Bit error rate
Licenses
Cross layer design
Resource management
Communication systems
Semantic communications
pre-trained language model
power allocation
data importance
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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