信息工程大学学报2024，Vol.25Issue(4)：379-383,5.DOI:10.3969/j.issn.1671-0673.2024.04.001
一种利用深度学习的非均匀无记忆信源恢复方法
Signal Recovery for Non-uniform Non-memory Sources Utilizing Deep Learning
摘要
Abstract
Aiming at the symbol recovery problem at the receiver side for the special natural redun-dancy sources of non-uniform non-memory sources,a neural network decoder architecture is proposed,which is based on the fully connected neural network model.The architecture incorporates the signal-to-noise ratio of the received signal and the symbol distribution of the memoryless source along with the received data as inputs to the model.An iterative decoding algorithm based on this neural network model is proposed to realize the natural redundancy decoding in the case of unknown distributions of transmitted symbols.The simulation results show that the symbol detection performance at the receiver side can be improved by using natural redundancy.Moreover,the optimal performance can be theoreti-cally obtained by the proposed algorithm,even when the source distribution is unknown.关键词
自然冗余/符号检测/非均匀无记忆信源/深度学习Key words
natural redundancy/symbol detection/non-uniform non-memoryless source/deep learning分类
信息技术与安全科学引用本文复制引用
王振玉,菅春晓,刘成城,赵安军,王彦生,王亚杰..一种利用深度学习的非均匀无记忆信源恢复方法[J].信息工程大学学报,2024,25(4):379-383,5.基金项目
国家自然科学基金(62171468) （62171468）
