WAVELET ANALYSIS OF MODULATED SIGNALS

The relationship between Haar wavelet decomposition coefficients and modulated signal parameters is discussed. A new modulation classification method is presented. The new method uses the amplitude, frequency and phase information derived from Haar wavelet decomposition as feature vectors to distinguish the modulation types of M-ary Frequency-Shift Keying （MFSK）, M-ary Phase-Shift Keying （MPSK） and Quadrature Amplitude Modulation （QAM） modulation types. A parallel combined classifier is designed based on these feature vectors. The overall successful recognition rate of 92.4% can be achieved even at a low Sig- nal-to-Noise Ratio （SNR） of 5dB.