Modulation Signal Classification and Recognition Algorithm Based on Signal to Noise Ratio Classification Network

GUO Yecai; YAO Wenqiang

doi:10.11999/JEIT210825

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3507-3515

Jin Sui-Geng. ENUMERATING ALL HAMILTONIAN CYCLES IN SOME GRAPHS BY USING THE GENERALIZED FIBONACCI SEQUENCE AND ITS PRODUCTION RULE[J]. Journal of Electronics & Information Technology, 1983, 5(3): 147-154.

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GUO Yecai, YAO Wenqiang. Modulation Signal Classification and Recognition Algorithm Based on Signal to Noise Ratio Classification Network[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3507-3515. doi: 10.11999/JEIT210825

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Modulation Signal Classification and Recognition Algorithm Based on Signal to Noise Ratio Classification Network

doi: 10.11999/JEIT210825

GUO Yecai^,,
YAO Wenqiang

School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (61673222), The Graduate Innovation Practice Project of Wuxi Campus of Nanjing University of Information Science and Technology (WXCX201915)

Received Date: 2021-08-12
Accepted Date: 2021-12-07
Rev Recd Date: 2021-12-05

Available Online: 2021-12-11

Publish Date: 2022-10-19

Abstract

Abstract

Considering the problem that the traditional noise reduction algorithm damages the high Signal-to-Noise Ratio (SNR) signal and reduces the accuracy of signal recognition, a SNR classification algorithm based on convolutional neural network is proposed. The algorithm uses Convolutional Neural Network (CNN) to extract the features of the signal, and uses Fixed K-means (FK-means) algorithm to cluster the extracted features to classify accurately the high and low signal-to-noise ratio signals. The low SNR signal is denoised by the improved median filter algorithm. The improved median filter algorithm adds the correlation mechanism of the front and rear sampling windows on the basis of the traditional median filter to improve the poor effect of the traditional median filter algorithm in dealing with continuous noise. In order to extract the spatial and temporal features of signals fully, a Convolutional neural network and Long-short term memory Parallel (P-CL) network with convolutional neural network and long-short term memory in parallel is proposed. The spatial and temporal features of signals are extracted by convolutional neural network and long-short term memory respectively, and the features are fused and classified. Experiments show that the recognition accuracy of the modulation signal classification model proposed in this paper is 91%, which is 6% higher than that of Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM) network.

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References(21)

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