Retrieval-augmented Generation for GenAI-enabled Semantic Communications

TL;DR

The paper addresses semantic inconsistency, limited adaptability, and lack of knowledge accumulation in GenSemCom. It proposes a RAG-enabled GenSemCom architecture that injects external knowledge via a knowledge base, an intelligent retriever, and a knowledge-aware encoder/decoder to guide semantic encoding and decoding. A case study on image transmission using a diffusion-based GenSemCom system with multi-modal prompts demonstrates improved semantic fidelity and image reconstruction across varying $BER$ levels, quantified by metrics such as $CLIP$ similarity, $LPIPS$, $PIEAPP$, and $MS-SSIM$. The findings suggest that RAG significantly improves robustness and efficiency of GenSemCom, with future directions including adaptive retrieval, knowledge-base synchronization, and privacy/security considerations.

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.

Figures (6)

• Figure 1: Overview of the representative works about GenSemCom in the past two years (2023-2024), where yellow and blue are used to denote the works mainly focusing on using GenAI as the semantic encoder and decoder, respectively.
• Figure 2: Illustration of RAG. (a) The components and the typical procedure of RAG, including inputted prompt, retriever, and generator. (b) Different ways of integrating RAG in GenAI models including VAEs, GANs, transformers, and GDMs.
• Figure 3: Illustration of the proposed RAG-enabled GenSemCom system, where the intelligent retriever dynamically queries knowledge bases and the retrieved results are refined through interactive LLM reviews. Besides, a stop-exploration strategy is used to balance efficiency and relevance. The knowledge-aware semantic encoder and decoder use the retrieved information to transmit and reconstruct the content with high semantic consistency.
• Figure 4: Illustration of the proposed GDM-based SemCom system with RAG. Key steps include: (1) Multimodal semantic information extraction using LLMs for text prompts and Canny detector for edge maps as visual prompts; (2) Semantic information transmission after source coding and channel coding; (3) Information retrieval and prompt enhancement, retrieving related textual and visual information using RAG to enhance prompts; (4) Image reconstruction, using GDM with ControlNet to reconstruct high-quality images.
• Figure 5: Numerical results of the proposed RAG system with different configurations. (a) CLIP similarity across varying BERs on the Kodak dataset. (b) CLIP similarity across varying BERs on the West Lake image. (c) Ablation study on the West Lake image.