Retrieval-augmented Generation for GenAI-enabled Semantic Communications
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arXiv
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| Format: | Preprint |
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2024
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| author | Tang, Shunpu Zhang, Ruichen Yan, Yuxuan Yang, Qianqian Niyato, Dusit Wang, Xianbin Mao, Shiwen |
| author_facet | Tang, Shunpu Zhang, Ruichen Yan, Yuxuan Yang, Qianqian Niyato, Dusit Wang, Xianbin Mao, Shiwen |
| contents | Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communication efficiency. Recent advancements in generative artificial intelligence (GenAI) have empowered GenAI-enabled SemCom (GenSemCom) to further expand its potential in various applications. However, current GenSemCom systems still face challenges such as semantic inconsistency, limited adaptability to diverse tasks and dynamic environments, and the inability to leverage insights from past transmission. Motivated by the success of retrieval-augmented generation (RAG) in the domain of GenAI, this paper explores the integration of RAG in GenSemCom systems. Specifically, we first provide a comprehensive review of existing GenSemCom systems and the fundamentals of RAG techniques. We then discuss how RAG can be integrated into GenSemCom. Following this, we conduct a case study on semantic image transmission using an RAG-enabled diffusion-based SemCom system, demonstrating the effectiveness of the proposed integration. Finally, we outline future directions for advancing RAG-enabled GenSemCom systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19494 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Retrieval-augmented Generation for GenAI-enabled Semantic Communications Tang, Shunpu Zhang, Ruichen Yan, Yuxuan Yang, Qianqian Niyato, Dusit Wang, Xianbin Mao, Shiwen Networking and Internet Architecture Information Theory Signal Processing Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communication efficiency. Recent advancements in generative artificial intelligence (GenAI) have empowered GenAI-enabled SemCom (GenSemCom) to further expand its potential in various applications. However, current GenSemCom systems still face challenges such as semantic inconsistency, limited adaptability to diverse tasks and dynamic environments, and the inability to leverage insights from past transmission. Motivated by the success of retrieval-augmented generation (RAG) in the domain of GenAI, this paper explores the integration of RAG in GenSemCom systems. Specifically, we first provide a comprehensive review of existing GenSemCom systems and the fundamentals of RAG techniques. We then discuss how RAG can be integrated into GenSemCom. Following this, we conduct a case study on semantic image transmission using an RAG-enabled diffusion-based SemCom system, demonstrating the effectiveness of the proposed integration. Finally, we outline future directions for advancing RAG-enabled GenSemCom systems. |
| title | Retrieval-augmented Generation for GenAI-enabled Semantic Communications |
| topic | Networking and Internet Architecture Information Theory Signal Processing |
| url | https://arxiv.org/abs/2412.19494 |