Research Article
Deep Joint Source Channel Coding via Attention for Wireless Image Transmission
@INPROCEEDINGS{10.1007/978-3-031-86196-3_27, author={Haoze Chang and Lin Ma and Xuedong Wang}, title={Deep Joint Source Channel Coding via Attention for Wireless Image Transmission}, proceedings={Wireless and Satellite Systems. 14th EAI International Conference, WiSATS 2024, Harbin, China, August 23--25, 2024, Proceedings, Part I}, proceedings_a={WISATS}, year={2025}, month={3}, keywords={JSCC Deep Learning Wireless Image Transmission Attention Mechanisms}, doi={10.1007/978-3-031-86196-3_27} }- Haoze Chang
Lin Ma
Xuedong Wang
Year: 2025
Deep Joint Source Channel Coding via Attention for Wireless Image Transmission
WISATS
Springer
DOI: 10.1007/978-3-031-86196-3_27
Abstract
In digital communication, efficiently transmitting image and video data through constrained channels remains challenging nowadays. Traditional methods using separate source and channel coding often fail in dynamic environments. In this paper, we introduce a novel deep learning based (DL) attention joint source channel coding (AttenJSCC) approach, which enhances robustness and efficiency in wireless image transmissions. By integrating source and channel coding into a unified framework and incorporating our Enhanced Attention Feature (EAF) modules and the ECA attention mechanism, our method outperforms some of the existing JSCC techniques, especially in low SNR conditions. Our framework not only overcomes the limitations of current technologies but also reduces the storage and computational needs on edge devices, facilitating more efficient real time communication.
