DEVELOPMENT OF ENHANCED MACHINE LEARNING METHODS FOR THE AUTOMATIC MODULATION CLASSIFICATION OF QAM SIGNALS
| dc.contributor.author | Savenkov, Andrii | |
| dc.date.accessioned | 2025-09-19T08:23:23Z | |
| dc.date.available | 2025-09-19T08:23:23Z | |
| dc.date.issued | 2025-06-09 | |
| dc.description.abstract | This 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. | |
| dc.identifier.citation | Savenkov, Andrii. (2025). DEVELOPMENT OF ENHANCED MACHINE LEARNING METHODS FOR THE AUTOMATIC MODULATION CLASSIFICATION OF QAM SIGNALS. Kyiv: American University Kyiv. URI: | |
| dc.identifier.uri | https://er.auk.edu.ua/handle/234907866/158 | |
| dc.language.iso | en_US | |
| dc.publisher | Manuscript | |
| dc.subject | Automatic Modulation Classification | |
| dc.subject | Machine Learning | |
| dc.subject | Digital Signal Processing | |
| dc.subject | Convolutional Neural Networks | |
| dc.subject | Gaussian Mixture Models | |
| dc.subject | Higher Order Cumulants | |
| dc.subject | Software-Defined Radio | |
| dc.title | DEVELOPMENT OF ENHANCED MACHINE LEARNING METHODS FOR THE AUTOMATIC MODULATION CLASSIFICATION OF QAM SIGNALS | |
| dc.title.alternative | Розробка вдосконалених методів машинного навчання для автоматичної класифікації модуляції QAM-сигналів | |
| dc.type | Thesis |