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Abstract
This paper introduces two algorithms for analog and digital modulations recognition. The first algorithm utilizes the decision-theoretic approach in which a set of decision criteria for identifying different types of modulations is developed. In the second algorithm the artificial neural network (ANN) is used as a new approach for the modulation recognition process. Computer simulations of different types of band-limited analog and digitally modulated signals corrupted by band-limited Gaussian noise sequences have been carried out to measure the performance of the developed algorithms. In the decision-theoretic algorithm it is found that the overall success rate is over 94% at the signal-to-noise ratio (SNR) of 15 dB, while in the ANN algorithm the overall success rate is over 96% at the SNR of 15 dB.
Published Version
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