摘要
作为实现语义无线通信的关键技术之一,端到端联合源信道编码(Joint Source-Channel Coding,JSCC)取得了显著进展。JSCC严重依赖于深度学习模型,这些复杂的神经网络训练通常需要大量的计算资源,对于资源受限的平台,如移动或嵌入式设备,提出了重大挑战。通过介绍一种轻量级的语义无线图像传输方法——MobileJSCC,旨在显著降低计算复杂度,同时不降低通信质量,便于在移动或嵌入式系统上部署。实验验证了MobileJSCC在各种图像分辨率、信噪比和信道条件下,实现了与先进的JSCC方法相当的图像恢复性能,大大减少了网络模型所需的计算量和参数。
As one of the key technologies for realizing semantic wireless communication,end-to-end Joint Source-Channel Coding(JSCC)has made significant progress in recent years.JSCC relies heavily on deep learning models,and the training of these complex neural networks often demands substantial computational resources.This presents a significant challenge for resource constrained platforms,such as mobile or embedded devices.A lightweight semantic wireless communication network,namely MobileJSCC,is introduced to significantly reduce computational complexity without compromising communication quality,thereby facilitating deployment on mobile or embedded systems.Experiments have validated that MobileJSCC achieves image recovery performance comparable to that of state-of-the-art JSCC methods under various image resolutions,signal to noise ratios,and channel conditions,while greatly decreasing the computational operations and parameters required by the network model.
作者
黄军韬
赵响
香晏
HUANG Juntao;ZHAO Xiang;XIANG Yan(Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology,Guilin 541004,China)
出处
《无线电工程》
2025年第7期1422-1430,共9页
Radio Engineering
基金
国家自然科学基金地区科学基金项目(61961007)。
关键词
语义无线通信
联合源信道编码
轻量化
语义无线图像传输
semantic wireless communication
JSCC
lightweight
semantic wireless transmission of images
