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

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication Pub Date : 2025-02-01 DOI:10.1016/j.phycom.2024.102568

Xingyu Wang, Xiangdong Zheng, Lianhong Zhang, Chao Li

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引用次数: 0

Abstract

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.

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深度联合源信道编码增强了无线图像传输的双向中继网络

与传统的单向中继相比，双向中继网络为现代通信系统提供了重要的增强和优化。然而，随着人工智能应用对图像数据传输的要求越来越高，中继辅助通信技术在带宽方面达到了理论极限，阻碍了人工智能应用的进一步发展。为了解决这个问题，我们提出了一个深度联合源信道编码的双向中继网络（DeepJSCC-TWRN）来帮助图像传输。具体而言，在DeepJSCC-TWRN中，采用DeepJSCC从视觉语义信息的角度提高TWRN的图像传输质量，通过统一的深度学习框架对每个源进行训练，达到最优性能。为了测量所提出的DeepJSCC-TWRN的性能，我们采用峰值信噪比（PSNR）和结构相似性指数测量（SSIM）作为性能指标。仿真结果表明，DeepJSCC-TWRN优于基线方法，展示了保留视觉语义信息的能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS

CiteScore

5.00

自引率

9.10%

发文量

212

审稿时长

55 days

期刊介绍： PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published. Topics of interest include but are not limited to: Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.