Modulation Classification Based on Extensible Neural Networks

Abstract

A deep learning architecture based on Extensible Neural Networks is proposed for modulation classification in multipath fading channel. Expanded Neural Networks (ENN) are established based on energy natural logarithm model. The model is set up using hidden layers. Modulation classification based on ENN is implemented through the amplitude, phase, and frequency hidden network, respectively. In order to improve Probability of Correct classification (PCC), one or more communication signal features are extracted using hidden networks. Through theoretical proof, ENN learning network is demonstrated to be effective in improving PCC using amplitude, phase, and the frequency weight subnetwork, respectively. Compared with the traditional algorithms, the simulation results show that the proposed ENN has higher PCC than traditional algorithm for modulation classification within the same training sequence and Signal to Noise Ratio (SNR).