  Traditional communication systems focus on the transmission process, and the
context-dependent meaning has been ignored. The fact that 5G system has
approached Shannon limit and the increasing amount of data will cause
communication bottleneck, such as the increased delay problems. Inspired by the
ability of artificial intelligence to understand semantics, we propose a new
communication paradigm, which integrates artificial intelligence and
communication, the semantic communication system. Semantic communication is at
the second level of communication based on Shannon and Weaver\cite{6197583},
which retains the semantic features of the transmitted information and recovers
the signal at the receiver, thus compressing the communication traffic without
losing important information. Different from other semantic communication
systems, the proposed system not only transmits semantic information but also
transmits semantic decoder. In addition, a general semantic metrics is proposed
to measure the quality of semantic communication system. In particular, the
semantic communication system for image, namely AESC-I, is designed to verify
the feasibility of the new paradigm. Simulations are conducted on our system
with the additive white Gaussian noise (AWGN) and the multipath fading channel
using MNIST and Cifar10 datasets. The experimental results show that DeepSC-I
can effectively extract semantic information and reconstruct images at a
relatively low SNR.

Signal Processing