CLASSIFICATION OF MODULATION TECHNIQUES USING CONVOLUTIONAL NEURAL NETWORK: A REVIEW
Keywords:
Deep learning, Machine learning, Modulation classification, Multiclass classification, Wavelet transformAbstract
A method to classify the required modulations among the various kinds of distributed systems by using the Convolutional neural network (CNN). A supervised Machine learning (ML) algorithm, Support vector machine (SVM) is used to classify the required modulation among all the modulations due to its advantages of majorly low complexity. In this paper, different researchers’ research work is studied and different problems are faced like CNNs are a regularised version of multilayer perceptrons that were motivated by the biological process of neuronal connection. They are efficiently used in a variety of classification problems because, in contrast to other classification methods, they require less preparation. The CNN is explained in simple terms with relevant mathematical analysis. For a better understanding of the classification of modulated techniques, some analog modulation techniques such as Binary phase shift keying (BPSK), Quadrature phase shift keying (QPSK), 8-ary phase shift keying (8-PSK), 16-ary Quadrature amplitude modulation (16-QAM), 64-ary Quadrature amplitude modulation (64-QAM), 4-ary pulse amplitude modulation (PAM4), Gaussian frequency shift keying (GFSK), Continuous phase frequency shift keying (CPFSK) and digital modulations such as Broadcast FM (B-FM), Double sideband amplitude modulation (DSB-AM), Single sideband amplitude modulation (SSB-AM) are considered for review.
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