Communication Signal Modulation Pattern Recognition Technology

Automatic recognition of modulation signals is the key problem in non_cooperative communication systems. It is the required function of Software Defined Radio Receiver. With rapidly developing of communication technology, the system and modulation manner of communication signals have become more and more complicated and circumstance of signals has become increasing denseness. It results in that the routine methods and theory of recognition can hardly satisfy practical requirements and can't effectively recognize communication signals. So the strict demand has been presented for studying on recognition of communication signals.For the last several decade years, the people have helpfully explored many methods of solving the question of recognition of communication signals. This thesis presents the time domain character and frequency domain character of common used signals, including digital modulation signals and analogue modulation signals. The first method is to extract feature parameters from time domain and frequency domain. It can distinguish 2ASK, 4ASK, 2FSK, 4FSK, BPSK, QPSK, 16QAM, 64QAM and it is demonstrated by simulation data and sampled data. Then, the definition of high order cumulants and the value of digital modulation signals'high order cumulants are presented. According to calculate the value of base band signals'high order cumulants, 4ASK, 2FSK, 4FSK, BPSK, QPSK, 16QAM can be distinguished. This method is demonstrated by simulation data and sampled data. It can be implemented in lower SNR condition. In addition, the thesis presents the theory of wavelet transform and method to recognize DSB, 2ASK, FM/PM, 2FSK, 2PSK by discrete wavelet transform. It is demonstrated by simulation data. As for classifier, it presents the artificial neural network. Based on three methods of modulation recognition and decision tree classifier and neural network classifier, experimentations have been carried through.The experimental results are satisfying, which are gained in different SNR condition. Those results reflect the capability and performance of different methods. The thesis summarizes their strongpoint and weakness.