QAM Modulation Classification using Constellation diagram based on TTSAS Algorithm and Template Matching

Majlesi Journal of Electrical Engineering

مقاله 3، دوره 2، شماره 4، دی 2008   

نوع مقاله: Review Article

شناسه دیجیتال (DOI): 10.1234/mjee.v2i4.134

نویسندگان

Negar Ahmadi^* ؛ Reza Berangi

چکیده

Recently the problem of modulation classification has received much attention in military and commercial applications. Various approaches introduced to solve this problem. Most of these approaches has been based on some special characteristics of received signal which are resolvable for various types of modulations. In this paper modulated signal symbols constellation utilizing TTSAS clustering algorithm and matching with standard templates, is used for classification of QAM modulation. TTSAS algorithm used in this paper is implemented by Hamming neural network. The simulation results show the capability of this method for modulation classification with high accuracy and appropriate convergence in the presence of noise.   

کلیدواژه‌ها

TTSAS Clustering Algorithm؛ en؛ template matching؛ Hamming Neural Network؛ Automatic Modulation Recognition

مراجع

[1] A. B. Carlson, P. B. Crilly, J. C. Rutledge;

“Communication system”, McGraw Hill,

Fourth edition, 2001.

[2] Y. Yang, S. S. Soliman; “An improved

moment-based algorithm for signal

classification”, signal processing, Vol. 43,

1995, pp. 231-244 .

[3] B. Mobaseri; “ Digital modulation

Classification using constellation shape”,

Signal Processing pp. 251-277, Jan 2000 .

[4] S. S. Soliman, S. Hsue; “Signal classification

using statistical moments”, IEEE Trans. On

Communication, Vol. COM 40, No. 5, pp.

908-916, May 1992.

[5] J. Lopatka and M. Pedzisz; “Automatic

modulation classification using statistical

moments and a fuzzy classifier”, Signal

Processing Proceedings ,WCCC- ICSP 2000,

5th international conf. ' on, 21-25 Aug. 2000,

Vol.3, pp. 1500-1506.

[6] M. L. D. Wong, A. K. Nandi; “Automatic

modulation recognition using spectral and

statistical features with multi layer

perceptrons”, Sixth international symposium on

Signal processing and its application,, Aug.

2001, Vol. 2, pp.390-393.

[7] Y. O. Al-Jalili; “ Identification algorithm of

upper sideband and lower sideband SSB

signals”, Signal Processing, Vol. 42, 1995, pp.

207-213.

[8] Kim, Kiseon, Polydoros; “Digital modulation

recognition: the BPSK versus QPSK case”,

MILCOM '88, Oct. 1988, Vol. 2, pp.431-436 .

[9] J. A. Sills, Maximum-likelihood modulation

classification for PSK/'OAM, MILCOM '99,

Conf. Proceedings, 31 Oct.-3 Nov. ,1999, Vol.

1, pp. 217-220.

[10] E. E. Azzouz, A. K. Nandi; “Automatic

modulation recognition of communication

signals”, Kluwer Academic Publishers,

Boston, 1996.

[11] A. K. Nandi, E. E. Azzouz; “Algorithms for

automatic modulation recognition of

communication signals”, IEEE Trans. On

Communication, Vol. 46, No. 4, pp. 431-436,

1998.

[12] E. E. Azzouz, A. K. Nandi; “ Automatic

identification of digital modulation types”

Signal Processing , Vol. 47, pp. 55-69, 1995.

[13] N. Ghani, R. Lamontagne; “ Neural networks

applied to the classification of spectral

features for automatic modulation

recognition”, MILCOM, 1993, Vol. 1, pp.

111-115 .

[14] B. Mobaseri, “Constellation shape as a

robust signature for digital modulation

recognition”, Military Communications

Conference Proceedings, MILCOM IEEE

,Volume 1, Issue ,pp. 442-446, 1999.

[15] Sergios Theodoridis, Konstantions

Koutroumbas; “Pattern Recognition”,

Second Edition , Elsevier Academic Press,

2003.

[16] Earl Gose, Richard Johnsonbaugh, Steve Jost;

“Pattern Recognition and Image Analysis”,

Prentice Hall PTR , 1996.