Title :
Algorithms for automatic modulation recognition of communication signals
Author :
Nandi, Asoke K. ; Azzouz, E.E.
Author_Institution :
Dept. of Electron. & Electr. Eng., Strathclyde Univ., Glasgow, UK
fDate :
4/1/1998 12:00:00 AM
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
Keywords :
Gaussian noise; decision theory; modulation; neural nets; pattern recognition; sequences; signal processing; ANN algorithm; SNR; analog modulation; artificial neural network; automatic modulation recognition; bandlimited Gaussian noise sequences; bandlimited modulated signals; communication signals; computer simulations; decision-theoretic algorithm; digital modulation; performance; signal-to-noise ratio; success rate; Amplitude modulation; Artificial neural networks; Computer simulation; Digital modulation; Feature extraction; Frequency shift keying; Pattern recognition; Phase modulation; RF signals; Signal to noise ratio;
Journal_Title :
Communications, IEEE Transactions on
