  Semantic communication has become a popular research area due its high
spectrum efficiency and error-correction performance. Some studies use deep
learning to extract semantic features, which usually form end-to-end semantic
communication systems and are hard to address the varying wireless
environments. Therefore, the novel semantic-based coding methods and
performance metrics have been investigated and the designed semantic systems
consist of various modules as in the conventional communications but with
improved functions. This article discusses recent achievements in the
state-of-art semantic communications exploiting the conventional modules in
wireless systems. We demonstrate through two examples that the traditional
hybrid automatic repeat request and modulation methods can be redesigned for
novel semantic coding and metrics to further improve the performance of
wireless semantic communications. At the end of this article, some open issues
are identified.
