AGI-Driven Generative Semantic Communications: Principles and Practices
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911013049204736 |
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| author | Yuan, Xiaojun Ma, Haoming Huang, Yinuo Hua, Zhoufan Zuo, Yong Ding, Zhi |
| author_facet | Yuan, Xiaojun Ma, Haoming Huang, Yinuo Hua, Zhoufan Zuo, Yong Ding, Zhi |
| contents | Semantic communications leverage artificial intelligence (AI) technologies to extract semantic information for efficient data delivery, thereby significantly reducing communication cost. With the evolution towards artificial general intelligence (AGI), the increasing demands for AGI services pose new challenges to semantic communications. In this context, an AGI application is typically defined on a general-sense task, covering a broad, even unforeseen, set of objectives, as well as driven by the need for a human-friendly interface in forms (e.g., videos, images, or text) easily understood by human users.In response, we introduce an AGI-driven communication paradigm for supporting AGI applications, called generative semantic communication (GSC). We first describe the basic concept of GSC and its difference from existing semantic communications, and then introduce a general framework of GSC based on advanced AI technologies including foundation models and generative models. Two case studies are presented to verify the advantages of GSC. Finally, open challenges and new research directions are discussed to stimulate this line of research and pave the way for practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14947 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AGI-Driven Generative Semantic Communications: Principles and Practices Yuan, Xiaojun Ma, Haoming Huang, Yinuo Hua, Zhoufan Zuo, Yong Ding, Zhi Artificial Intelligence Image and Video Processing Signal Processing Semantic communications leverage artificial intelligence (AI) technologies to extract semantic information for efficient data delivery, thereby significantly reducing communication cost. With the evolution towards artificial general intelligence (AGI), the increasing demands for AGI services pose new challenges to semantic communications. In this context, an AGI application is typically defined on a general-sense task, covering a broad, even unforeseen, set of objectives, as well as driven by the need for a human-friendly interface in forms (e.g., videos, images, or text) easily understood by human users.In response, we introduce an AGI-driven communication paradigm for supporting AGI applications, called generative semantic communication (GSC). We first describe the basic concept of GSC and its difference from existing semantic communications, and then introduce a general framework of GSC based on advanced AI technologies including foundation models and generative models. Two case studies are presented to verify the advantages of GSC. Finally, open challenges and new research directions are discussed to stimulate this line of research and pave the way for practical applications. |
| title | AGI-Driven Generative Semantic Communications: Principles and Practices |
| topic | Artificial Intelligence Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2504.14947 |