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Blind Modulation Identification Using Machine Learning Algorithms

P.G Varna Kumar Reddy, P. Mythreyi, Y. Prathyusha, K. Thanuja, B. Hemanth Kumar

Abstract


One fast developing technology that has potential use in software defined radio architecture, particularly in 5G and 6G networks, is blind modulation identification, which is also called automatic modulation classification (AMC). For systems operating at low signal-to-noise ratios (SNRs), Machine Learning (ML) offers innovative and efficient technology for modulation classification. Intelligent wireless communication system receivers, particularly those for adaptive radio systems, rely on modulation format recognition. Classes for higher-order digital modulation signals have been constructed using a number of different methods, including KNearestNeighbors, DecisionTree, Bagging, and Pasting classifiers. This research seeks to find the optimal model for achieving superior accuracy in modulation classification within the given context. To further prove the efficiency of these classifiers for modulation classification, we test their performance against one another. Decision Tree Classifier outperformed other ML classifiers with an accuracy of 82%.

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