Automatic recognition and demodulation of digitally modulated communications signals using wavelet-domain signatures

Abstract

Wavelet transform-based methodologies for both Automatic Modulation Recognition (AMR) and Demodulation of digitally modulated communications signals can be utilized in an enabling platform for the implementation of a new class of communications systems. In particular, such techniques could enable the development of agile radio transceivers for use in both commercial and military applications. Such radio transceivers would have the ability to transmit and receive signals using many different modulation schemes while employing a common receiver architecture based on a single demodulator. In this dissertation, the development of AMR and Demodulation techniques are based on the relatively new mathematical theory of Wavelet Transforms (WTs). Information-bearing signals acquired by communications receivers are transformed into the wavelet-domain using the Continuous Wavelet Transform (CWT) and then applied to signal processing algorithms that also use the CWT in conjunction with pattern recognition techniques. In particular, the method of template-matching is used for both the AMR and Demodulation processes. Signal templates characterizing various modulated signals are used for both processes. The signal templates are determined based on the signal features present in the fractal patterns of their corresponding scalograms for specific modulation schemes as they appear in the wavelet-domain. The algorithms developed in this work are capable of both classifying the method of modulation used in the acquired signal, as well as subsequently automatically demodulating the signal to recover the message. The classes of digitally modulated signals considered in this work include variants of the Amplitude-, Frequency-, Phase-Shift Keying modulation families, i.e., ASK, FSK, and PSK, respectively, and multiple-level Quadrature Amplitude Modulation (M-ary QAM) families. The AMR and Demodulation performances are evaluated in the presence of Additive White Gaussian Noise (AWGN) over a wide range of Signal-to-Noise Ratio (SNR) values. Through extensive Monte Carlo computer simulations it is determined that the average correct classification rates using wavelet-based AMR for PSK, ASK, and QAM are over 98%, and over 90% for FSK signals, all at an SNR of 0 dB. The Bit Error Rate (BER) performance obtained using wavelet-based Demodulation is at least one order of magnitude better than the matched filter-based BER performance realized for the modulation schemes considered.