Work

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

Year: 2025

Type: article

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.

Source: ICT Express

DOI: 10.1016/j.icte.2025.02.010

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

Institution: Chung-Ang University

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500