Abstract—The back propagation (BP) neural networks have been commonly used for automatic modulation recognition since the late 1990s. However, the back propagation algorithm easily falls into local minimum and the network learning is sensitive to initial weight values which usually determined by experience. The particle swarm optimization (PSO) algorithm is a global heuristic searching technology. By combing these two techniques, this paper presents a novel automatic modulation classifier. By training BP neural network with PSO optimization, this classifier can identify seven digital signals with six input features. The experiment results show that this recognizer achieves about 92% recognition performance at the SNR of 0dB.
Index Terms—automatic modulation classifier, particle swarm optimization, back propagation neural network, parameter optimizations.
Cite: Li Cheng and Jin Liu, "Automatic Modulation Classifier Using Artificial Neural Network Trained by PSO Algorithm," Journal of Communications, vol. 8, no. 5, pp. 322-329, 2013. Doi: 10.12720/jcm.8.5.322-329
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