移动通信2026，Vol.50Issue(5)：37-43,7.DOI:10.3969/j.issn.1006-1010.20260304-0002
基于DenseNet的相关噪声抑制辅助译码算法
DenseNet-Based Correlation Noise Suppression-Assisted Decoding Algorithm
摘要
Abstract
In practical mobile communication systems,correlated noise can significantly degrade the performance of belief propagation(BP)decoding algorithms.To address this issue,a channel denoising-aided decoding algorithm based on a dense convolutional network(DenseNet)is proposed.Without changing the basic framework of BP decoding,a plug-and-play DenseNet denoising module is introduced to enhance feature propagation and fusion through dense connections,thereby learning the correlated noise structure more effectively.The system adopts a cascaded structure of BP prior decoding,DenseNet residual denoising,and BP iterative decoding.A composite loss function combining mean square error and normality constraints is employed to reduce noise power while preserving the Gaussian property of residual noise.Simulation results based on low-density parity-check codes show that,when the noise correlation coefficient is 0.8,the proposed scheme achieves approximately 3 dB performance gain over conventional BP decoding without denoising and a further gain of about 0.5 dB over conventional convolutional neural network-aided schemes,verifying its effectiveness and application potential in correlated noise channels.关键词
置信传播译码/相关噪声/密集连接网络/降噪辅助译码Key words
belief propagation decoding/correlated noise/Dense convolutional network/denoising-assisted decoding分类
信息技术与安全科学引用本文复制引用
徐晓林,袁晓威,刘希,夏斌,魏岳军..基于DenseNet的相关噪声抑制辅助译码算法[J].移动通信,2026,50(5):37-43,7.基金项目
重庆市自然科学基金创新发展联合基金(中国星网)(CSTB2024NSCQ-LMX008) （中国星网）
