Retrieval-augmented Generation for GenAI-enabled Semantic Communications

TL;DR

This paper explores integrating retrieval-augmented generation (RAG) with generative AI-enabled semantic communications to improve adaptability, consistency, and efficiency in transmitting task-relevant semantic information.

Contribution

It introduces a novel approach combining RAG techniques with GenAI-enabled semantic communication systems, supported by a case study on semantic image transmission.

Findings

RAG integration enhances semantic consistency.

Improves adaptability to diverse tasks and environments.

Demonstrates effectiveness in semantic image transmission.

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.