A New Automatic Modulation Classifier Using Swarm Intelligence-Trained Neural Network

Abstract

Monitoring modulation type of the detected signal is the most important intermediate step between signal detection and demodulation. The back propagation neural network (BPNN) was widely used in constructing modulated signal classifier in the field of automatic modulation classification (AMC). There are many visible features in the back propagation (BP) algorithm including adaptive learning, the ability of fault tolerant, etc. However, this algorithm has two main disadvantages, such as the slow convergence speed and easily falling into the local minimum. This paper presents a novel modulation classifier using BPNN trained with swarm intelligence algorithms (SIA), for the sake of overcoming these deficiencies. The initial weights and thresholds of BP neural network were optimized by SIA. As the SIA has an excellent global search property, this classifier can consume less training time and improve the automatic modulation type identification rate.