Automatic Modulation Recognition using the Discrete Wavelet Transform

An Automatic Modulation Recognition (AMR) process using the Discrete Wavelet Transform (DWT) is presented in this work. The AMR algorithm involves the use of wavelet domain signal templates derived from digitally modulated signals that are used to transmit binary data. The signal templates, locally stored in a receiver, are cross-correlated with the incoming noisy, received signal after it has been transformed into the wavelet domain. The signal template that yields the largest cross-correlation value determines the type of digital modulation that had been employed at the transmitter. The specific binary-valued digital modulation schemes considered in this work include BASK, BFSK and BPSK. The discrete wavelet used for the creation of the signal templates is the Haar, or Daubechies 1, wavelet. Extensive computer simulations have been performed to evaluate the modulation recognition performance of the AMR algorithm as a function of channel SNR. It has been determined that the rate of correct classification for BASK signals is 68% for an SNR = 5 dB and 90% for an SNR = 10 dB SNR. The rate of correct classification for BFSK signals is 71% for an SNR = 5 dB and 92% for an SNR = 10 dB. Correct classification of BPSK signals is 71% for an SNR = 5 dB and 92% for an SNR = 10 dB. In comparison to alternative AMR methods reported in the literature, iii the AMR algorithm developed in this study produces reliable results even at relatively low values of SNR which are characteristic of realistic communications channels.