Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model. (arXiv:2210.15237v1 [eess.SP])

While semantic communication is expected to bring unprecedented communication
efficiency in comparison to classical communication, many challenges must be
resolved to realize its potential. In this work, we provide a realistic
semantic network dubbed seq2seq-SC, which is compatible to 5G NR and can work
with generalized text dataset utilizing pre-trained language model. We also
utilize a performance metric (SBERT) which can accurately measure semantic
similarity and show that seq2seq-SC achieves superior performance while
extracting semantically meaningful information.

arxiv communication language language model semantic seq2seq systems