Related papers: Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model

Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model

URL: http://arxiv.org/abs/2210.15237v2
Date: Wed, 18 Oct 2023 00:15:10 GMT
Title: Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model
Authors: Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen, Thomas Choi, Jay Pujara
Abstract summary: We propose a realistic semantic network called seq2seq-SC, designed to be compatible with 5G NR. We employ a performance metric called semantic similarity, measured by BLEU for lexical similarity and SBERT for semantic similarity.
Score: 20.925910474226885
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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