Classification of modulation signals using statistical signal characterization and artificial neural networks

  • Authors:

  • Abdulnasir Hossen;Fakhri Al-Wadahi;Joseph A. Jervase

  • Affiliations:

  • Department of Electrical and Computer Engineering, Sultan Qaboos University, P.O. Box 33 Al-Khod, 123 Muscat, Oman;Department of Electrical and Computer Engineering, Sultan Qaboos University, P.O. Box 33 Al-Khod, 123 Muscat, Oman;Department of Electrical and Computer Engineering, Sultan Qaboos University, P.O. Box 33 Al-Khod, 123 Muscat, Oman

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2007

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Abstract

Modulation recognition systems have to be able to correctly classify the incoming signal modulation scheme in the presence of noise. A new method for classification of analogue and digital modulated signals at low signal-to-noise ratio (SNR) is introduced in this paper. This method uses the statistical signal characterization (SSC) to extract parameters to classify the different modulation signals. The SSC technique produces a set of four numerical parameters for a specific modulated signal. Subsequent comparison of these parameters to those of other waveforms provides the basis for our classification system. The results of SSC technique are applied to an artificial neural network (ANN) to have a robust classification system in the presence of noise down to SNR of 3dB. No a priori information is required by this technique about the set of input waveforms. The input to the classification system can be analogue or digital signals or a combination of both. The proposed technique shows a 100% efficiency of classification of analogue signals or digital signals at SNR of 7dB. This classification efficiency reduces to 83% and 86% for analogue or digital signals at SNR of 3dB. The SSC technique shows better classification results in comparison with other techniques with an important advantage over other methods, which is the simplicity of the neural network needed with this technique due to the small number of features used in the classification.