  Recently, deep learned enabled end-to-end (E2E) communication systems have
been developed to merge all physical layer blocks in the traditional
communication systems, which make joint transceiver optimization possible.
Powered by deep learning, natural language processing (NLP) has achieved great
success in analyzing and understanding large amounts of language texts.
Inspired by research results in both areas, we aim to providing a new view on
communication systems from the semantic level. Particularly, we propose a deep
learning based semantic communication system, named DeepSC, for text
transmission. Based on the Transformer, the DeepSC aims at maximizing the
system capacity and minimizing the semantic errors by recovering the meaning of
sentences, rather than bit- or symbol-errors in traditional communications.
Moreover, transfer learning is used to ensure the DeepSC applicable to
different communication environments and to accelerate the model training
process. To justify the performance of semantic communications accurately, we
also initialize a new metric, named sentence similarity. Compared with the
traditional communication system without considering semantic information
exchange, the proposed DeepSC is more robust to channel variation and is able
to achieve better performance, especially in the low signal-to-noise (SNR)
regime, as demonstrated by the extensive simulation results.
