Retrieval-augmented Generation for GenAI-enabled Semantic Communications
Journal:
arXiv
Published Date:
Dec 27, 2024
Abstract
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.
