Online modulation recognition of analog communication signals using neural network

In this paper, a neural network based online analog modulation recognition of communication signals is presented. The proposed system can discriminate between amplitude modulation (AM), frequency modulation (FM), double sideband (DSB), upper sideband (USB), lower sideband (LSB) and continuous wave (CW) modulations. A matlab graphical user interface (GUI) is designed to see the intercepted signal, its power spectral density, frequency and modulation type on the screen of the personnel computer. To achieve correct classification, extensive simulations have been done for training the neural network. Theoretical simulations and experimental results indicate good performance even at signal-to-noise ratios as low as 5dB.

[1]  Friedrich K. Jondral,et al.  Automatic classification of high frequency signals , 1985 .

[2]  José Manuel Páez-Borrallo,et al.  A general approach to the automatic classification of radiocommunication signals , 1991, Signal Process..

[3]  A. K. Nandi,et al.  Procedure for automatic recognition of analogue and digital modulations , 1996 .

[4]  Samir S. Soliman,et al.  Automatic modulation classification using zeroç crossing , 1990 .

[5]  Y. O. Al-Jalili.  Identification algorithm of upper sideband and lower sideband SSB signals , 1995, Signal Process..

[6]  Abdulkadir Sengür,et al.  Comparison of clustering algorithms for analog modulation classification , 2006, Expert Syst. Appl..

[7]  D. Boudreau,et al.  An automatic modulation recognition algorithm for spectrum monitoring applications , 1999, 1999 IEEE International Conference on Communications (Cat. No. 99CH36311).

[8]  Elsayed Elsayed Azzouz,et al.  Modulation recognition using artificial neural networks , 1997, Signal Process..

[9]  Yiu-Tong Chan,et al.  Identification of the modulation type of a signal , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Janet Aisbett.  Automatic modulation recognition using time domain parameters , 1987 .

[11]  Stefan C. Kremer,et al.  A testbed for automatic modulation recognition using artificial neural networks , 1997, CCECE '97. Canadian Conference on Electrical and Computer Engineering. Engineering Innovation: Voyage of Discovery. Conference Proceedings.