Savenkov, Andrii2025-09-192025-09-192025-06-09Savenkov, Andrii. (2025). DEVELOPMENT OF ENHANCED MACHINE LEARNING METHODS FOR THE AUTOMATIC MODULATION CLASSIFICATION OF QAM SIGNALS. Kyiv: American University Kyiv. URI:https://er.auk.edu.ua/handle/234907866/158This study evaluates multiple machine learning approaches for Automatic Modulation Classification (AMC) of five digital modulation schemes: BPSK, QPSK, 8PSK, 8QAM, and 16QAM. The research implements and compares four classification methods: Gaussian Mixture Models, multinomial regression with polynomial features, nearest neighbor approach, and Convolutional Neural Networks. The system is integrated into a web service using Elixir/Phoenix framework and Python. Experimental results demonstrate high accuracy across all methods, with CNNs achieving 99% accuracy on simulated signals and consistent 99% accuracy on real signals from a CDM-625 modem. The study provides a practical solution for deployment in modern communication systems.en-USAutomatic Modulation ClassificationMachine LearningDigital Signal ProcessingConvolutional Neural NetworksGaussian Mixture ModelsHigher Order CumulantsSoftware-Defined RadioThesis