.png){kind=icon}
返回
GatedNet: Neural Network Decoding for Decoding Over Impulsive Noise Channels
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2019.2920638.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This letter proposes a novel neural network (NN) called GatedNet for decoding over-impulsive noise channels. To reduce the impact of impulsive noise, the neurons in the GatedNet are redesigned with control gates to achieve better performance and robustness. Furthermore, a partially connected layer (PCL) in the neural network design is proposed to reduce the computational complexity. Simulation results show that the proposed GatedNet decoder outperforms the belief propagation (BP) decoder for different impulsive noise channels. Furthermore, it achieves near-maximum likelihood (ML) performance with much shorter decoding time.
Keyword:
Deep learning
low-density parity-check codes
channel decoding
impulsive noise channels
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
查看更多机构
引用论文
Cholic acid-based high sensitivity fluorescent sensor for α,ω-dicarboxylate: an intramolecular excimer emission quenched by complexation.png)
Tetrahedron
TetrahedronIF0
PREAI
分享收藏
没有更多内容