  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.
