Communication signal modulation recognition based on cyclic spectrum features and bagged decision tree

Abstract

With the increasing diversification of signal modulation types, the importance of signal modulation recognition is increasing, which is an important part between signal detection and demodulation. It has great applied value in jamming identification, electronic countermeasures, intelligent modem and other fields. Aiming at the improvement of recognition accuracy for some modulation types, a communication signal modulation recognition method based on cyclic spectrum features and bagged decision tree is proposed. The method extracts the cyclic spectrum features of signals and inputs them into the bagged decision tree for model training. Simulation results show that the accuracy of the proposed method reaches 93.8%, which is 39.4% higher than that of the traditional recognition method with high-order cumulants and 22.2% higher than that of the method using the original signal directly.