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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.00124 |
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| _version_ | 1866916083249709056 |
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| author | Liang, Chengsi Du, Hongyang Sun, Yao Niyato, Dusit Kang, Jiawen Zhao, Dezong Imran, Muhammad Ali |
| author_facet | Liang, Chengsi Du, Hongyang Sun, Yao Niyato, Dusit Kang, Jiawen Zhao, Dezong Imran, Muhammad Ali |
| contents | Generative artificial intelligence (GAI) has emerged as a rapidly burgeoning field demonstrating significant potential in creating diverse contents intelligently and automatically. To support such artificial intelligence-generated content (AIGC) services, future communication systems should fulfill much more stringent requirements (including data rate, throughput, latency, etc.) with limited yet precious spectrum resources. To tackle this challenge, semantic communication (SemCom), dramatically reducing resource consumption via extracting and transmitting semantics, has been deemed as a revolutionary communication scheme. The advanced GAI algorithms facilitate SemCom on sophisticated intelligence for model training, knowledge base construction and channel adaption. Furthermore, GAI algorithms also play an important role in the management of SemCom networks. In this survey, we first overview the basics of GAI and SemCom as well as the synergies of the two technologies. Especially, the GAI-driven SemCom framework is presented, where many GAI models for information creation, SemCom-enabled information transmission and information effectiveness for AIGC are discussed separately. We then delve into the GAI-driven SemCom network management involving with novel management layers, knowledge management, and resource allocation. Finally, we envision several promising use cases, i.e., autonomous driving, smart city, and the Metaverse for a more comprehensive exploration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00124 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications Liang, Chengsi Du, Hongyang Sun, Yao Niyato, Dusit Kang, Jiawen Zhao, Dezong Imran, Muhammad Ali Signal Processing Generative artificial intelligence (GAI) has emerged as a rapidly burgeoning field demonstrating significant potential in creating diverse contents intelligently and automatically. To support such artificial intelligence-generated content (AIGC) services, future communication systems should fulfill much more stringent requirements (including data rate, throughput, latency, etc.) with limited yet precious spectrum resources. To tackle this challenge, semantic communication (SemCom), dramatically reducing resource consumption via extracting and transmitting semantics, has been deemed as a revolutionary communication scheme. The advanced GAI algorithms facilitate SemCom on sophisticated intelligence for model training, knowledge base construction and channel adaption. Furthermore, GAI algorithms also play an important role in the management of SemCom networks. In this survey, we first overview the basics of GAI and SemCom as well as the synergies of the two technologies. Especially, the GAI-driven SemCom framework is presented, where many GAI models for information creation, SemCom-enabled information transmission and information effectiveness for AIGC are discussed separately. We then delve into the GAI-driven SemCom network management involving with novel management layers, knowledge management, and resource allocation. Finally, we envision several promising use cases, i.e., autonomous driving, smart city, and the Metaverse for a more comprehensive exploration. |
| title | Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2401.00124 |