TranGDeepSC: Leveraging ViT knowledge in CNN-based semantic communication system

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express Pub Date : 2025-03-08 DOI:10.1016/j.icte.2025.02.010

Tung Son Do, Thanh Phung Truong, Quang Tuan Do, Sungrae Cho

引用次数: 0

Abstract

This paper introduces TranGDeepSC, a lightweight CNN-based deep semantic communication (DeepSC) system that leverages Vision Transformer (ViT) knowledge through co-training to enhance image transmission. Evaluated on CIFAR-100 across various SNRs, TranGDeepSC demonstrates competitive performance with ViTDeepSC, and outperforms SemViT and ADJSCC-V in image quality, particularly in low-SNR environments. Notably, it offers substantial gains in efficiency: 92.8% fewer parameters than ADJSCC-V, 72.0% lower energy use, and 48% faster processing than ViTDeepSC. These advantages make TranGDeepSC well-suited for resource-constrained applications in next-generation communication systems, including 6G, IoT, and real-time multimedia streaming.

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来源期刊

ICT Express Multiple-

CiteScore

10.20

自引率

1.90%

发文量

167

审稿时长

35 weeks

期刊介绍： The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.