A Contemporary Survey on Semantic Communications: Theory of Mind, Generative AI, and Deep Joint Source-Channel Coding
Semantic communication is emerging as a key pillar of wireless communication technology, owing to its capabilities in reducing communication overhead, enhancing resilience to channel noise, and supporting a large number of users. However, it still encounters various challenging obstacles that need t...
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| Vydané v: | IEEE Communications surveys and tutorials s. 1 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
2025
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| Predmet: | |
| ISSN: | 2373-745X |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Semantic communication is emerging as a key pillar of wireless communication technology, owing to its capabilities in reducing communication overhead, enhancing resilience to channel noise, and supporting a large number of users. However, it still encounters various challenging obstacles that need to be solved before real-world deployment. The major challenge is the lack of standardization across different directions, leading to variations in interpretations and objectives. In the survey, we provide detailed explanations of three leading directions in semantic communications, namely Theory of Mind , Generative AI , Deep Joint Source-Channel Coding . These directions have been widely studied, developed, and verified by institutes worldwide, and their effectiveness has increased along with the advancement in technology. We provide explanations of the concepts and background for each direction. Firstly, we introduce the Theory of Mind-based semantic communication, in which communication agents gradually develop a shared, minimal-length language through interactions within a shared environment. Subsequently, we present works on Generative AI-based semantic communication, which leverage the creativity of generative models to produce high-quality data for goal-oriented tasks, as well as their powerful data encoding capabilities to represent transmitted information with minimal redundancy. For the final direction, we highlight the importance of deep learning (DL) models in jointly optimizing source and channel coding, demonstrating their ability to overcome the cliff effect associated with traditional separate coding approaches. Then, we present a comprehensive survey of existing works in each direction, thereby offering readers an overview of past achievements and potential avenues for further contribution. Moreover, for each direction, we identify and discuss the existing challenges that must be addressed before these approaches can be effectively deployed in real-world scenarios. These challenges encompass a range of technical, computational, and practical issues. For instance, scalability and adaptability remain critical barriers in real-world deployment, particularly in dynamic environments with diverse user demands. Additionally, the possibility of applying emerging technology, specifically quantum computing, in semantic communication is discussed in this survey. |
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| ISSN: | 2373-745X |
| DOI: | 10.1109/COMST.2025.3616973 |