Automatic Modulation Classification Using a Support Vector Machine-Based Pattern Recognition Algorithm

Automatic Modulation Classification Using a Support Vector Machine-Based Pattern Recognition Algorithm Varna Kumar Reddy P. G. Meena M.

Modulation format recognition is an essential part of intelligent receivers of wireless communication systems, especially for adaptive radio systems (ARS). This paper presents a detailed investigation of automatic modulation classification (AMC) using pattern recognition classifiers (PRC) under fading and AWGN conditions. A variety of classifiers with different kernel functions and Support Vector Machine (SVM) classifiers have been developed for the classification of higher-order digital modulation signals. In addition, an extensive investigation of the extraction of various higher-order statistical features from each of the modulated classes and the choice of appropriate features for training classifiers are presented. In addition, the performance of the SVM classifier is evaluated under a variety of training rates and suboptimal channel conditions. Further, the performance of SVM classifiers is compared to that of existing techniques to demonstrate the effectiveness of the SVM classifiers for modulation categorization.
12 31 2022 12 31 2022 999 1007 Crossmark v2.0 10.18280/CrossmarkPolicy www.iieta.org true 22 October 2022 3 December 2022 31 December 2022 http://iieta.org/sites/default/files/TEXT%20AND%20DATA%20MINING%20SERVICE%20AGREEMENT.pdf 10.18280/isi.270617 https://www.iieta.org/journals/isi/paper/10.18280/isi.270617 https://www.iieta.org/journals/isi/paper/10.18280/isi.270617