  With the development of deep learning (DL), natural language processing (NLP)
makes it possible for us to analyze and understand a large amount of language
texts. Accordingly, we can achieve a semantic communication in terms of joint
semantic source and channel coding over a noisy channel with the help of NLP.
However, the existing method to realize this goal is to use a fixed transformer
of NLP while ignoring the difference of semantic information contained in each
sentence. To solve this problem, we propose a new semantic communication system
based on Universal Transformer. Compared with the traditional transformer, an
adaptive circulation mechanism is introduced in the Universal Transformer.
Through the introduction of the circulation mechanism, the new semantic
communication system can be more flexible to transmit sentences with different
semantic information, and achieve better end-to-end performance under various
channel conditions.
