This paper describes how end-to-end communication systems can improve classification task accuracy and reduce transmission bandwidth requirements through joint training optimization.Two channel models were considered in the study,the additive Gaussian white noise(AWGN)channel and the slow fading channel.The simulation evaluation of the communication system was conducted on the CIFAR-10 dataset,and the results showed that the system can adaptively select the best encoding and modulation strategies,ensuring transmission quality while saving transmission resources.Meanwhile,compared with traditional digital communication systems,this communication system avoids the"cliff effect",performs well under large-scale SNR fluctuations,and has strong robustness.Under the same bandwidth conditions,the jointly optimized communication system has improved accuracy by 30％compared to the traditional communication system using source channel separation coding.
关键词
端到端通信系统/深度学习/联合信源-信道编码/码率控制/图像分类任务
Key words
end-to-end communication system/deep learning/joint source channel coding/rate control/image classification task
维普
万方数据