Abstract
In the past decades, there was a growing need for the automatic classification that is related to digital signal formats, that also appears to be on going tendency in the future. Automatic modulation recognition (AMR) is considered to be of high importance in military and civil applications and communication systems. The recognition regarding the received signal modulation can be defined as a transitional stage between detection and demodulation of signals. in this paper, several features which are associated with the received signal will be extracted and used. Which is of high importance in increasing the AMR’s effectiveness. Algorithms from Chicken Swarm optimization and Bat Swarm optimization were used to improve the features of modulated signals and thus increase the accuracy of the classification. It then classifies the features of the modified signals resulting from the optimization algorithms by a random forest. The results showed that swarm Chickens algorithm performs better than the Bat swarm algorithm, even at level SNR low, Results From chicken Algorithm & Random forest classifier were Accuracy of classification 95% while Accuracy of classification From Bat Algorithm & Random forest 91%.
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Alhadi, B.A., Hasan, T.M., Hamed, H.A. (2020). Digitally Modulated Signal Recognition Based on Feature Extraction Optimization and Random Forest Classifier. In: Al-Bakry, A., et al. New Trends in Information and Communications Technology Applications. NTICT 2020. Communications in Computer and Information Science, vol 1183. Springer, Cham. https://doi.org/10.1007/978-3-030-55340-1_6
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