Abstract:Background/Objectives: The ability to recognize the type of modulation is a critical function of Cognitive Radio. The objective of this study is to increase the modulation classification efficiency in Over-The-Air (OTA) signals by utilizing channel characteristics that are strong. Methods: In this work, we demonstrate how to classify Over-The-Air modulation using a deep learning technique under various fading channels simulating real-time data. The network recognizes eight different digital modulation schemes … Show more
“…From the detailed literature, it is also found that some of the other approaches have also been developed for AMC. They are statistical approaches [17][18][19][20][21][22], where different statistical features of the signal, such as correlations, moments, and cumulants in the complex envelope of the signal, are extracted and then a multilevel classification algorithm is applied for classifying the signals. The accuracy of the Back Propagation Neural Network (BPNN) is higher than that of the Kolmogorov Smirnov (KS) and higher-order statistics (HoS) approaches [23].…”
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.
“…From the detailed literature, it is also found that some of the other approaches have also been developed for AMC. They are statistical approaches [17][18][19][20][21][22], where different statistical features of the signal, such as correlations, moments, and cumulants in the complex envelope of the signal, are extracted and then a multilevel classification algorithm is applied for classifying the signals. The accuracy of the Back Propagation Neural Network (BPNN) is higher than that of the Kolmogorov Smirnov (KS) and higher-order statistics (HoS) approaches [23].…”
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.
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