Performance assessment of a machine-learning-derived digital RF communication classifier
Steve T. Kacenjar, Aaron Dant, Ronald A. Neely · 2020
A study is performed to gauge the effectiveness of training a Machine Learning (ML) System for Automatic Modulation Classification (AMC) to accurately identify several diverse digital communication transmission types occurring across the High Frequency (HF) Radio Frequency (RF) spectrum. This study uniquely uses Software Defined Radio (SDR) Power Spectral Density (PSD) waterfall signatures to help classify nine common types of amateur radio digital communication modes. Such an approach provides an alternative to more traditional In-phase/Quadrature (IQ) methods which can require large training sets. LeNet and ResNet Convolutional Neural Network (CNN) models are examined. Training/validation sets sensitivities are examined through Monte Carlo methods. Additionally, performances are examined in terms of confusion matrices as a function of Signal-to-Noise Ratio (SNR).