[1]张刚灿,李苹苹,申金媛.基于半监督聚类理论的MQAM信号的调制识别[J].郑州大学学报(工学版),2014,35(04):83-87.[doi:10.3969/j.issn.1671-6833.2014.04.020]
　SUN Gangcan,LI Pingping,SHEN Jinyuan,et al.Modulation Recognition of MQAM Signals Based onSemi-supervised Clustering Theory[J].Journal of Zhengzhou University (Engineering Science),2014,35(04):83-87.[doi:10.3969/j.issn.1671-6833.2014.04.020]
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基于半监督聚类理论的MQAM信号的调制识别()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
35卷
期数:
2014年04期
页码:
83-87
栏目:
出版日期:
2014-08-30

文章信息/Info

Title:
Modulation Recognition of MQAM Signals Based onSemi-supervised Clustering Theory
作者:
张刚灿; 李苹苹; 申金媛
Author(s):
SUN Gangcan; LI Pingping; SHEN Jinyuan; etc;
School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Keywords:
emi-supervised clustering;  modulation classifieation;  constellation diagram;  support vector machine
分类号:
TN911.23
DOI:
10.3969/j.issn.1671-6833.2014.04.020
文献标志码:
A
Abstract:
In the modulation classilieation of MOAM signals, clustering points based on traditional clusteringalgorithm is nol aeeurate. The number of iterations of the algorithm is more and the error sum of squares fune.tion curve is not smooth. To solve this problem, this paper presents a MOAM signal modulation recognitionmethod based on semi-supervised clustering theory to reeonstruet signal constellation diagram. By markingsome sample points to guide the membership and updates of the cluster centers, combined with SVM classifiea.tion , the different levels of MOAM signal’s reeognition are realized. The simulation results show that the algo.rithm for MOAM signal recognition rate is greater than 90% , has less iteration and the error sum of squaresfunclion curve is smooth.
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