Abstract
This paper presents an efficient digital modulation classification method based on wavelet transform and artificial neural networks (ANN). The method performs feature extraction via the discrete wavelet transform of the underlying digital signals because of the usefulness of the wavelet in de-noising and in compressing the digital signals. The features extracted from wavelet coefficients are then presented to the ANN for pattern recognition and classification. In addition, a less nodes output player and error back propagation learning with momentum are used to speed up the training process and improve the convergence of the ANN. Experimental results and performance evaluation of the method are given and it is found that the benefits of the developed method are that its structure is simple and it performs well at low signal to noise ratio (SNR) with high overall success rates.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dabbous, T.E., Shafie, K.A.: A Dynamic Model for Neural Networks. In: 16th National Radio Science Conference (NRSC 1999), Ain Shams University, Cairo, Egypt, pp. 1–10 (1999)
Nandi, A.K., Azzouz, E.E.: Algorithms for Automatic Modulation Recognition of Communication Signals. IEEE Transactions on Communications 46, 431–436 (1998)
Kavalov, D.: Improved Noise Characteristics of a Saw Artificial Neural Network RF Signal Processor for Modulation Recognition. In: 2001 IEEE Ultrasonics Symposium, pp. 19–22 (2001)
Kremer, S.C., Shiels, J.: A Testbed for Automatic Modulation Recognition Using Artificial Neural Networks. In: CCECE 1997, pp. 67–70 (1997)
Habibi, A.: Introduction to Wavelets. IEEE, 879–885 (1995)
Hoffman, A.J., Tollig, C.J.A.: The Application of Classification Wavelet Networks to the Recognition of Transient Signals. IEEE, 407–410 (1999)
Stefan, P., Kamarthi, S.V.: Feature Extraction from Wavelet Coefficients for Pattern Recognition Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 21 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Z., Ren, G., Wang, X., Zhao, Y. (2004). Automatic Digital Modulation Recognition Using Wavelet Transform and Neural Networks. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_154
Download citation
DOI: https://doi.org/10.1007/978-3-540-28647-9_154
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive