Intelligent Digital Signal Modulation Recognition using Machine Learning

Inrecent years, modulation type recognition has received a lot of attentionacross the board. There are several methods to detect modulation types, butthere are only a few effective methods to handle signals with a higher level ofnoise. This study introduces an approach to verify the ability of differentmachine learning algorithms to automatically manage noise in detecting digitalmodulations. This research examines two of the most common digital modulations,Phase Shift Keying and Quadrature Phase Shift Keying. A signal noise rateranging from-10 to +25 dB is used to identify these modulations and use PCA toreduce the number of features and the data complexity. We used machine learningalgorithms like Decision Tree, Random Forest, Support Vectors Machine andk-nearest neighbors to identify the modulation type. Our proposed methodconsiders signals' features after proper shifting of the IQ and RF signalssamples. The findings show that the proposed method successfully recognizes thesignals with higher noise levels. The random forest algorithm presents better resultswith low noise levels and SVM with higher noise levels.