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Cnn-based automatic modulation recognition for index modulation systems

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Akademik Birimler

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Cicioğlu, Murtaza

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Leblebici, Merih
Çalhan, Ali

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Pergamon-elsevier Science Ltd

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Automatic modulation recognition (AMR) has garnered significant attention in both civilian and military domains, with applications ranging from spectrum sensing and cognitive radio (CR) to the deterrence of adversary communication. Index modulation (IM) represents an innovative digital modulation technique that exploits the indices of parameters of communication systems to transmit extra information bits. This paper aims to examine the performance of a convolutional neural network (CNN)-based AMR across various IM systems, including spatial modulation (SM), quadrature spatial modulation (QSM), and generalized spatial modulation (GSM) with eight digital modulation schemes. In this study, we leverage confusion matrices, receiver operating characteristic (ROC) curves, and F1 scores to illustrate the recognition model's outputs.

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Spatial modulation, Classification, Performance, Ofdm, Automatic modulation recognition, Convolutional neural network, Index modulation, Machine learning, Science & technology, Technology, Computer science, artificial intelligence, Engineering, electrical & electronic, Operations research & management science, Computer science, Engineering, Operations research & management science

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