Summary
2019 Joint International Symposium on Electromagnetic Compatibility and Asia-Pacific International Symposium on Electromagnetic Compatibility, Sapporo
2019
Session Number:ThuAM2A
Session:
Number:ThuAM2A.5
Modulation Classification of VHF Communication System Based on CNN and Cyclic Spectrum Graphs
Hao Wu, Yaxing Li, Yu Guo, Liang Zhou, Jin Meng,
pp.-
Publication Date:2016/10/5
Online ISSN:2188-5079
DOI:10.34385/proc.58.ThuAM2A.5
Summary:
Modulation classification is the technological basis of adaptive interference mitigation in communication system. This paper proposes a modulation classification method for very high frequency (VHF) signals, which is based on deep convolutional neural network (CNN) and cyclic spectrum graphs. First, the cyclic spectrum of VHF signals is analyzed. Then, a deep learning method based on CNN is proposed, down-sampling and clipping technologies are used for preprocessing cyclic spectrum images, parameters of the proposed neural network are optimized, and finally the modulation classification is realized. The experimental results show that, the proposed method has high modulation classification accuracy and less computation burden in low SNR.