End-to-End Generative Semantic Communication Powered by Shared Semantic Knowledge Base

TL;DR

This paper introduces a shared semantic knowledge base to enhance generative semantic communication for images, reducing transmission load and improving performance in resource-constrained and low SNR scenarios.

Contribution

The paper proposes a novel SKB-enabled system that guides semantic communication, enabling efficient image classification and controllable image generation with minimal transmission overhead.

Findings

Outperforms benchmarks in low SNR regimes

Reduces transmission load by transmitting only relevant indices

Achieves superior semantic accuracy with the proposed method

Abstract

Semantic communication has drawn substantial attention as a promising paradigm to achieve effective and intelligent communications. However, efficient image semantic communication encounters challenges with a lower testing compression ratio (CR) compared to the training phase. To tackle this issue, we propose an innovative semantic knowledge base (SKB)-enabled generative semantic communication system for image classification and image generation tasks. Specifically, a lightweight SKB, comprising class-level information, is exploited to guide the semantic communication process, which enables us to transmit only the relevant indices. This approach promotes the completion of the image classification task at the source end and significantly reduces the transmission load. Meanwhile, the category-level knowledge in the SKB facilitates the image generation task by allowing controllable generation, making it possible to generate favorable images in resource-constrained scenarios. Additionally, semantic accuracy is introduced as a new metric to validate the performance of semantic transmission powered by the SKB. Evaluation results indicate that the proposed method outperforms the benchmarks and achieves superior performance with minimal transmission overhead, especially in the low SNR regime.