  Wireless communication has achieved great success in the past several
decades. The challenge is of improving bandwidth with limited spectrum and
power consumption, which however has gradually become a bottleneck with
evolution going on. The intrinsic problem is that communication is modeled as a
message transportation from sender to receiver and pursues for an exact message
replication in Shannon's information theory, which certainly leads to large
bandwidth and power requirements with data explosion. However, the goal for
communication among intelligent agents, entities with intelligence including
humans, is to understand the meaning or semantics underlying data, not an
accurate recovery of the transmitted messages. The separate first transmission
and then understanding is a waste on bandwidth. In this article, we deploy
semantics to solve the spectrum and power bottleneck and propose a first
understanding and then transmission framework with high semantic fidelity. We
first give a brief introduction of semantics covering the definition and
properties to show the insights and scope of this paper. Then the proposed
communication towards semantic fidelity framework is introduced, which takes
the above mentioned properties into account to further improve efficiency.
Specially, a semantic transformation is introduced to transform the input into
semantic symbols. Different from the conventional transformations in signal
processing area, for example discrete cosine transform, the transformation is
with data loss, which is also the reason that the proposed framework can
achieve large bandwidth saving with high semantic fidelity. Besides, we also
discuss semantic noise and performance measurement. To evaluate the
effectiveness, a case study of audio transmission is carried out. Finally, we
discuss the typical applications and open challenges.
