Semantic Communications and Networking

Won Joon Yun, Soohyun Park, Rhoan Lee, Jihong Park, Young Chai Ko, Joongheon Kim

Research output: Chapter in Book/Report/Conference proceedingChapter


Until fifth-generation communications, researchers focused on transmitting the data fast and accurately for many users. For this, many studies have been shown around the relay system, with achievable rate maximization considering the co-interference. These are the first Shannon communication level, which focuses on bit transmission. Spurred by the advance of deep learning, the fusion of artificial intelligence (AI) and communications or networks emerged. In the second and third level of the Shannon-Weaver model, contextual meaning and reasoning are the objectives of transmission. Fortunately, AI has shown tremendous empirical results for contextual reasoning. In the physical (PHY) layer, joint source-channel coding (JSCC) is studied broadly. The variational autoencoder shows a strong background in research. In this chapter, we introduce the concepts of semantic communication (SC) and semantic networking. In addition, we introduce the performance enhancements such as loss of information, rate maximization, and robustness of noisy channels. Moreover, we provide the performance gain cited from research papers.

Original languageEnglish
Title of host publicationSignals and Communication Technology
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
Publication statusPublished - 2024

Publication series

NameSignals and Communication Technology
VolumePart F1944
ISSN (Print)1860-4862
ISSN (Electronic)1860-4870

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Autoencoder
  • NLP
  • Reinforcement learning
  • Semantic communications
  • Transformer

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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