Deep joint source-channel coding empowered two-way relay networks for wireless image transmission

Compared with the traditional uni-directional relaying, two-way relay networks provide important enhancements and optimizations to modern communication systems. However, with the increasing requirements of artificial intelligence applications for image data transmission, relay-assisted communication technologies are reaching the theoretical limit in terms of bandwidth, which hinders the further development of AI applications. To address this issue, we propose a deep joint source-channel coding empowered two-way relay network (DeepJSCC-TWRN) to help image transmission. Specifically, in the DeepJSCC-TWRN, a DeepJSCC is employed to improve image transmission quality of the TWRN from the perspective of visual semantic information, and each source can achieve optimal performance by being trained in a uniform deep learning framework. For measuring the performance of the proposed DeepJSCC-TWRN, we employ the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) as performance metrics. Simulation results show that DeepJSCC-TWRN outperforms the baseline method, demonstrating the ability to preserve visual semantic information.