In this work, we propose a realistic semantic network called seq2seq-SC, designed to be compatible with 5G NR and capable of working with generalized text datasets using a pre-trained language model. The goal is to achieve unprecedented communication efficiency by focusing on the meaning of messages in semantic communication. We employ a performance metric called semantic similarity, measured by BLEU for lexical similarity and SBERT for semantic similarity. Our findings demonstrate that seq2seq-SC outperforms previous models in extracting semantically meaningful information while maintaining superior performance. This study paves the way for continued advancements in semantic communication and its prospective incorporation with future wireless systems in 6G networks.

翻译：本文提出一种名为Seq2Seq-SC的实用化语义网络，该网络设计兼容5G NR（新空口）标准，并能够利用预训练语言模型处理通用文本数据集。其目标是通过聚焦于语义通信中消息的涵义实现前所未有的通信效率。我们采用语义相似度作为性能指标——通过BLEU衡量词汇级相似度，并利用SBERT评估语义级相似度。实验结果表明，Seq2Seq-SC在提取语义层有意义信息方面优于现有模型，同时保持卓越的通信性能。本研究为语义通信技术的持续发展，以及其未来与6G网络中无线系统的潜在融合奠定了基础。