.png){kind=icon}
返回
A Serial Layered Scheduling Algorithm for Factor Graph Equalization
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2014.2317745.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 factor graph equalization based on a serial layered scheduling (SLS) algorithm of belief propagation, where the channel convolutional matrix is decomposed according to the distribution of its non-zero elements. In the SLS algorithm based factor graph equalization, factor nodes are classified into multiple layers referring to the decomposition of channel convolutional matrix, and the a posteriori probabilities message is updated based on a serial layer-by-layer mechanism. This proposed SLS algorithm clearly decreases the signal-to-noise ratio (SNR) threshold and also increases the convergence speed of equalization process, which results in a higher reliability and a lower latency of equalization process. Compared with current parallel flooding scheduling (PFS) algorithm, the convergence speed of SLS algorithm increases almost by twice, and its SNR threshold reduces by 7 dB over the Proakis-C channel.
Keyword:
Factor graph equalization
serial layered scheduling (SLS)
signal-to-noise ratio (SNR) threshold
convergence speed
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
引用论文
.png){kind=icon}
分享收藏
查看更多内容