Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio...Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.展开更多
A new method suited for hardware implementation is developed to classify 8 different digital modulation types with raised cosine base-band impulse without knowing the carrier frequency and symbol timing. The normalize...A new method suited for hardware implementation is developed to classify 8 different digital modulation types with raised cosine base-band impulse without knowing the carrier frequency and symbol timing. The normalized histogram of stagnation points for instantaneous parameters is used to recognize both ideal rectangular and raised cosine base-band digital signals. Carrier frequency estimation is used to enhance the recognition rate of phase-modulated signals. In the condition of 10 dB signal noise ratio （SNR）, the recognizing rate is over 80% . The new algorithm is suited for hardware implementation.展开更多
文摘Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
文摘A new method suited for hardware implementation is developed to classify 8 different digital modulation types with raised cosine base-band impulse without knowing the carrier frequency and symbol timing. The normalized histogram of stagnation points for instantaneous parameters is used to recognize both ideal rectangular and raised cosine base-band digital signals. Carrier frequency estimation is used to enhance the recognition rate of phase-modulated signals. In the condition of 10 dB signal noise ratio （SNR）, the recognizing rate is over 80% . The new algorithm is suited for hardware implementation.
