基于深度学习的无线图像跨层优化传输方法综述
Review of Deep Learning-Driven Cross-Layer Optimization Methods Wireless Image Transmission
DOI: 10.12677/csa.2026.164129, PDF,   
作者: 叶兴涛, 宋佳铭：赣南师范大学数学与计算机科学学院，江西 赣州；王 敏*：赣南师范大学智能制造与未来能源学院，江西 赣州；江西省教育厅数据科学与人工智能重点实验室，江西 赣州
关键词: 无线图像传输；跨层优化；深度学习；自适应机制；Wireless Image Transmission； Cross-Layer Optimization； Deep Learning； Adaptive Transmission
摘要: 随着5G/6G技术的快速发展，高清图像、虚拟现实等新兴应用对无线图像传输的速率、实时性与可靠性提出了更高要求。然而，无线信道的时变特性使传统分离式的信源与信道编码方法在恶劣环境下性能急剧下降。为此，文章综述了基于深度学习的无线图像跨层优化传输方法的研究进展，系统回顾了从传统分离传输到端到端跨层联合优化的演变过程，重点介绍了自适应机制在无线图像跨层优化传输方法中的应用。同时，也指出了当前研究在更加复杂信道、实际部署以及其他传输场景等方面面临的挑战。
Abstract: With the rapid development of 5G/6G technologies, emerging applications such as high-definition images and virtual reality have imposed higher requirements for the rate, real-time performance, and reliability of wireless image transmission. However, the time-varying characteristics of wireless channels cause the performance of traditional separated source and channel coding methods to drop sharply in harsh environments. Therefore, this article reviews the research progress of wireless image cross-layer optimization transmission methods based on deep learning, systematically examines the evolution process from traditional separated transmission to end-to-end joint optimization, and focuses on introducing the application of adaptive mechanisms in wireless image cross-layer optimization transmission methods. At the same time, it also points out the challenges faced by current research in more complex channels, actual deployment, and other transmission scenarios.
文章引用：叶兴涛, 宋佳铭, 王敏. 基于深度学习的无线图像跨层优化传输方法综述[J]. 计算机科学与应用, 2026, 16(4): 274-286. https://doi.org/10.12677/csa.2026.164129

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