Algorithms for automatic modulation recognition of communication signals Export

@article{664294, 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}, author = {Nandi, A. K. and Azzouz, E. E.}, citeulike-article-id = {2817232}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/26.664294}, doi = {10.1109/26.664294}, journal = {Communications, IEEE Transactions on}, keywords = {analog-modulation, artificial-neural-network, automatic-modulation-recognition, bandlimited-gaussian-noise-sequences, bandlimited-modulated-signals, communication-signals, computer-simulations, decision-theoretic-algorithm, decision-theory, digital-modulation, gaussian-noise, modulation, neural-nets, pattern-recognition, performance, sequences, signal-classification, signal-processingann-algorithm, signal-to-noise-ratio, snr, success-rate}, number = {4}, pages = {431--436}, posted-at = {2008-05-20 18:27:45}, priority = {2}, title = {Algorithms for automatic modulation recognition of communication signals}, url = {http://dx.doi.org/10.1109/26.664294}, volume = {46}, year = {Apr 1998} }

TY - JOUR ID - 664294 L3 - citeulike-article-id:2817232 N2 - 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 IS - 4 JF - Communications, IEEE Transactions on EP - 436 TI - Algorithms for automatic modulation recognition of communication signals VL - 46 SP - 431 KW - analog-modulation KW - artificial-neural-network KW - automatic-modulation-recognition KW - bandlimited-gaussian-noise-sequences KW - bandlimited-modulated-signals KW - communication-signals KW - computer-simulations KW - decision-theoretic-algorithm KW - decision-theory KW - digital-modulation KW - gaussian-noise KW - modulation KW - neural-nets KW - pattern-recognition KW - performance KW - sequences KW - signal-classification KW - signal-processingann-algorithm KW - signal-to-noise-ratio KW - snr KW - success-rate AU - Nandi, AK AU - Azzouz, EE PY - Apr 1998/// UR - http://dx.doi.org/10.1109/26.664294 ER -