Helaly, T., Elbarbary, K. (2006). AUTOMATIC CLASSIFICATION OF QUADRATURE AMPLITUDE MODULATED (QAM) SIGNALS IN PRESENCE OF CHANNEL IMPAIRMENTS. test, 5(5th International Conference on Electrical Engineering ICEENG 2006), 1-11. doi: 10.21608/iceeng.2006.33694
Tarek Helaly; Khairy Elbarbary. "AUTOMATIC CLASSIFICATION OF QUADRATURE AMPLITUDE MODULATED (QAM) SIGNALS IN PRESENCE OF CHANNEL IMPAIRMENTS". test, 5, 5th International Conference on Electrical Engineering ICEENG 2006, 2006, 1-11. doi: 10.21608/iceeng.2006.33694
Helaly, T., Elbarbary, K. (2006). 'AUTOMATIC CLASSIFICATION OF QUADRATURE AMPLITUDE MODULATED (QAM) SIGNALS IN PRESENCE OF CHANNEL IMPAIRMENTS', test, 5(5th International Conference on Electrical Engineering ICEENG 2006), pp. 1-11. doi: 10.21608/iceeng.2006.33694
Helaly, T., Elbarbary, K. AUTOMATIC CLASSIFICATION OF QUADRATURE AMPLITUDE MODULATED (QAM) SIGNALS IN PRESENCE OF CHANNEL IMPAIRMENTS. test, 2006; 5(5th International Conference on Electrical Engineering ICEENG 2006): 1-11. doi: 10.21608/iceeng.2006.33694

AUTOMATIC CLASSIFICATION OF QUADRATURE AMPLITUDE MODULATED (QAM) SIGNALS IN PRESENCE OF CHANNEL IMPAIRMENTS

The International Conference on Electrical Engineering
Article 87, Volume 5, 5th International Conference on Electrical Engineering ICEENG 2006, May 2006, Pages 1-11    PDF (171.63 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2006.33694
Authors
Tarek Helaly1; Khairy Elbarbary2
1Associate Lecturer, Electronic Warfare Eng. Department, MTC, Cairo, Egypt.
2Associate Professor, Electronic Warfare Eng. Department, MTC, Cairo, Egypt.
Abstract
Abstract
In this paper, a procedure for automatic classification among three types of QAM signals in
presence of channel impairments is proposed. A combination of digital signal processing and
pattern recognition methods is used for solving the classification problem. The proposed
algorithm has been tested by computer simulations and has proven to be reliable and robust
against the expected channel impairments such as noise and fading. It is found that all digital
modulation types of interest have been correctly classified with a success rate > 91 % at
signal-to-noise ratio (SNR) of 14 dB.
Keywords
signal processing; Modulation Recognition; and Automatic Signal Classification
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