期刊
TSINGHUA SCIENCE AND TECHNOLOGY
卷 27, 期 2, 页码 422-431出版社
TSINGHUA UNIV PRESS
DOI: 10.26599/TST.2020.9010057
关键词
modulation recognition; deep learning; Gated Recurrent Unit (GRU); Convolutional Neural Network (CNN)
类别
资金
- Major Scientific and Technological Achievements Transformation Project of Heilongjiang Province [CG20A007]
Based on a comparative analysis of LSTM and GRU networks, this paper optimizes the structure of GRU network and proposes a new modulation recognition method based on feature extraction and deep learning algorithm. The proposed method achieves high recognition rate at low SNR.
Based on a comparative analysis of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio (SNR), instantaneous feature, and the cyclic spectrum of signals are extracted firstly, and then input into the Convolutional Neural Network (CNN) and the parallel network of GRU for recognition. Eight modulation modes of communication signals are recognized automatically. Simulation results show that the proposed method can achieve high recognition rate at low SNR.
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