Digital modulation recognition

Erdem, Erem
In this thesis work, automatic recognition algorithms for digital modulated signals are surveyed. Feature extraction and classification algorithm stages are the main parts of a modulation recognition system. Performance of the modulation recognition system mainly depends on the prior knowledge of some of the signal parameters, selection of the key features and classification algorithm selection. Unfortunately, most of the features require some of the signal parameters such as carrier frequency, pulse shape, time of arrival, initial phase, symbol rate, signal to noise ratio, to be known or to be extracted. Thus, in this thesis, features which do not require prior knowledge of the signal parameters, such as the number of the peaks in the envelope histogram and the locations of these peaks, the number of peaks in the frequency histogram, higher order moments of the signal are considered. Particularly, symbol rate and signal to noise ratio estimation methods are surveyed. A method based on the cyclostationarity analysis is used for symbol rate estimation and a method based on the eigenvector decomposition is used for the estimation of signal to noise ratio. Also, estimated signal to noise ratio is used to improve the performance of the classification algorithm. Two methods are proposed for modulation recognition: 1) Decision tree based method 2) Bayesian based classification method A method to estimate the symbol rate and carrier frequency offset of minimum-shift keying (MSK) signal is also investigated.


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Citation Formats
E. Erdem, “Digital modulation recognition,” M.S. - Master of Science, Middle East Technical University, 2009.