Residual Network Structure-based High Accuracy Spectral Analysis Method

doi:10.23940/ijpe.19.11.p29.30903098

Abstract

Abstract: The pivotal technology in spectrum analysis is the classification for normal communication signals and interference ones. Automatic modulation classification (AMC) is widely utilized to identify modulation types of received signals. In this paper, original signals with different modulation types are taken as the original input of the network. A convolutional neural network with residual network structure is designed to identify the modulation type. Meanwhile, a sliding window method is proposed to expand the data set. RadioML2016.a data sets are utilized for simulation, and the simulation results indicate that the complexity and accuracy of this method are better than those of recent methods.

Key words: automatic modulation classification, sliding window, residual network

Sai Wu, Zhihui Wang, Sachura Meng, Weijun Zheng, and Weiping Shao. Residual Network Structure-based High Accuracy Spectral Analysis Method [J]. Int J Performability Eng, 2019, 15(11): 3090-3098.

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