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- W3161083347 abstract "In recent years, some studies have shown that the deep learning method can be used to recognize the modulation mode of signal quickly and effectively, but the modulation recognition effect of low signal to noise rate (SNR) signal is obviously poor. This paper presented a signal modulation recognition method based on filtering technique and Convolution Neural Network (CNN) plus Long-Short Term Memory (LSTM) networks. First of all, signals are divided into high SNR signals and low SNR signals by second-order statistics of modulated signals, and Gaussian filtering is carried out for low SNR signals, so as to achieve the effect of noise reduction. Then, modulation recognition is carried out for all signals. Experimental results show that this method can recognize 11 types of modulation which including digital signals and analog signals, and the average recognition accuracy is 71.33%. When the SNR is greater than 0 dB, the average recognition accuracy can reach 96.53%. When the SNR is at -20 dB ∼ -8 dB, the recognition accuracy is also improved compared with the CNN method." @default.
- W3161083347 created "2021-05-24" @default.
- W3161083347 creator A5032618881 @default.
- W3161083347 creator A5060431284 @default.
- W3161083347 date "2020-12-18" @default.
- W3161083347 modified "2023-10-17" @default.
- W3161083347 title "Recognition of Signal Modulation Mode Based on Gaussian Filter and Deep Learning" @default.
- W3161083347 cites W2790896200 @default.
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- W3161083347 doi "https://doi.org/10.1145/3447654.3447662" @default.
- W3161083347 hasPublicationYear "2020" @default.
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