.png){kind=icon}
返回
Hybrid Maximum Likelihood Modulation Classification for Continuous Phase Modulations
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2016.2517007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this letter, we propose a hybrid maximum likelihood (HML) classifier for continuous phase modulation (CPM). To the best of our knowledge, the proposed likelihood function is the first one for CPM signals that is based on two of its main features: nonlinear waveform, which is represented with its principal components, and signal memory, which is modeled as a Markov mapping symbol sequence. Unknown channel parameters are estimated through the expectation-maximization (EM) algorithm. An approximation method is further proposed to ensure that the proposed classifier improves classification performance at the cost of a moderate increase in calculations. Numerical results prove the superiority of the proposed approach over the classical HML classifier and feature-based classifier in terms of classifying CPM and linear modulation.
Keyword:
Automatic modulation classification
CPM
ML estimation
EM algorithm
principal component analysis
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
暂无机构信息
引用论文
Selenocarbonyl complexes of iridium(I) and iridium(III). Synthesis and reactions of IrCl(CSe)(PPh3)2铱 (I) 和铱 (III) 的硒羰基配合物。IrCl(CSe)(PPh3)2的合成与反应
.png){kind=icon}
PREAI
分享收藏分享收藏
分享收藏 没有更多内容
没有更多内容