Modulation Recognition of MQAM Signals Based onSemi-supervised Clustering Theory
[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
35卷
Number of periods:
2014 04
Page number:
83-87
Column:
Public date:
2014-08-30
- Title:
- Modulation Recognition of MQAM Signals Based onSemi-supervised Clustering Theory
- Keywords:
- emi-supervised clustering; modulation classifieation; constellation diagram; support vector machine
- CLC:
- TN911.23
- DOI:
- 10.3969/j.issn.1671-6833.2014.04.020
- 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.
- References:
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