DEVELOPMENT OF ENHANCED MACHINE LEARNING METHODS FOR THE AUTOMATIC MODULATION CLASSIFICATION OF QAM SIGNALS

Savenkov, Andrii

DEVELOPMENT OF ENHANCED MACHINE LEARNING METHODS FOR THE AUTOMATIC MODULATION CLASSIFICATION OF QAM SIGNALS

Date

2025-06-09

Authors

Savenkov, Andrii

Publisher

Manuscript

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.

Keywords

Automatic Modulation Classification, Machine Learning, Digital Signal Processing, Convolutional Neural Networks, Gaussian Mixture Models, Higher Order Cumulants, Software-Defined Radio

Citation

Savenkov, Andrii. (2025). DEVELOPMENT OF ENHANCED MACHINE LEARNING METHODS FOR THE AUTOMATIC MODULATION CLASSIFICATION OF QAM SIGNALS. Kyiv: American University Kyiv. URI: