Recognition of communication signal types using genetic algorithm and support vector machines based on the higher order statistics

Digital Signal Processing: A Review Journal

Volumn 20, Issue 6, 2010, Pages 1748-1757

  Shermeh, Ataollah Ebrahimzadeh ^a   Ghazalian, Reza ^a  

^a BABOL NOSHIRVANI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Artificial intelligence; Communication signal type recognition; Higher order statistics; Model selection; Pattern recognition; Support vector machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; DIGITAL COMMUNICATION SYSTEMS; GENETIC ALGORITHMS; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES; VECTORS;

AUTOMATIC RECOGNITION; COMMUNICATION SIGNALS; DIGITAL COMMUNICATION SIGNALS; HIERARCHICAL SUPPORT VECTOR MACHINES; HIGHER ORDER CUMULANTS; INTELLIGENT TECHNIQUES; MODEL SELECTION; MULTI-CLASS CLASSIFIER;

HIGHER ORDER STATISTICS;

EID: 77955402526     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2010.03.003     Document Type: Article

References (23)

1. C. Le Martret, D. Boitea, A general maximum likelihood classifier for modulation classification, in: Proc. ICASSP, 1998, pp. 2165-2168
2. W. Wei, and J.M. Mendel Maximum-likelihood classification for digital amplitude-phase modulations IEEE Trans. Commun. 48 2000 189 193
3. P. Panagotiou, A. Polydoros, Likelihood ratio tests for modulation classifications, in: Proc. MILCOM, 2000, pp. 670-674
4. S.Z. Hsue, and S.S. Soliman Automatic modulation classification using zero-crossing IEE Proc. Radar Sonar Navigation 137 1990 459 464
5. B.G. Mobasseri Digital modulation classification using constellation shape Signal Process. 80 2000 251 277
6. E.E. Azzouz, and A.K. Nandi Automatic identification of digital modulations Signal Process. 47 1995 55 69
7. A. Swami, and B.M. Sadler Hierarchical digital modulation classification using cumulants IEEE Trans. Commun. 48 2000 416 429
8. J. Lopatka, P. Macrej, Automatic modulation classification using statistical moments and a fuzzy classifier, in: Proc. ICSP, 2000, pp. 121-127
9. N. Ghani, R. Lamontagne, Neural networks applied to the classification of spectral features for automatic modulation recognition, in: Proc. MILCOM, 1993, pp. 1494-1498
10. G. Hagedorn, B. James, C. Miller, Neural network recognition of signal modulation types, in: Proc. ANNIEC, 1997, pp. 170-175
11. A.K. Nandi, and E.E. Azzouz Algorithms for automatic modulation recognition of communication signals IEEE Trans. Commun. 46 1998 431 436
12. C.L.P. Sehier, Automatic modulation recognition with a hierarchical neural network, in: Proc. MILCOM, 1993, pp. 111-115
13. L. Mingquan, X. Xianci, L. Leming, Cyclic spectral features based modulation recognition, in: Proc. ICCT, 1996, pp. 792-795
14. A.E. Shermeh, D. Movahedy, A new method for identifying of signal type, in: Proc. ICIS, 2007
15. E. Avci, D. Hanbay, and A. Varol An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition Expert Syst. Appl. 33 3 2007 582 589
16. A.E. Shermeh, D. Movahedy, Automatic identification of PSK2 and PSK4 using SVM, in: ICIS 2008
17. C. Cortes, and V. Vapnic Support vector network Mach. Learn. 20 1995 1 25
18. C. Burges A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discov. 2 1998 121 167 (Pubitemid 128695475)
19. J.G. Proakis Digital Communications 2001 McGraw-Hill New York
20. P. McCullagh Tensor Methods in Statistics 1987 Chapman & Hall
21. B. Scholkopf, C. Burges, V. Vapnik, Extracting support data for a given task, in: ICKDDM, 1995, pp. 252-257
22. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee Choosing multiple parameters for support vector machines Mach. Learn. 46 1 2002 131 159 (Pubitemid 34129966)
23. Z. Michalewicz Genetic Algorithms + Data Structures = Evolution Programs 3rd edition 1999 Springer New York