Research And Simulation On Digital Modulation Recognition Of Wireless Communication System

With the rapid development of the communication technology,digital modulation recognition wireless communication has played an increasingly important role in the domain of communication and signal researches.Digital modulation recognition is the key technology of the signals demodulation,information extraction and signals detection in communication systems,so it is very important in both cooperative and non-cooperative communication fields.At present,modulation recognition algorithms mainly include two types:one is based on the statistics theory and the other is based on the theory of ruling.In this dissertation,the algorithm based on the statistical pattern recognition has been mainly studied.Firstly,this paper introduces common digital modulation signals and studies instantaneous information extraction based on Hilbert transform,the common algorithm of carrier frequency estimate and symbol rate estimation.Secondly,on the basis of existing digital signal feature extraction,an improved algorithm which extracts six features is presented on this paper.It compares characteristic parameters of signals and threshold values to recognize modulation types.And the simulation results indicate that the improved recognition algorithm is superior to the traditional algorithm.Thirdly,in order to overcome shortcomings of second order statistics,a new algorithm is proposed,namely as high-order cumulant.This algorithm can recognize six common digital signals.Simulation results show that the accurate identification rate of every signal is higher than 99%when SNR is 2dB,so the algorithms is still effective even when the SNR is very low.At last,this thesis introduces the wavelet transform theory and explains the nature of wavelet transform,transform of common digital signals as well as normalization of digital signals.The wavelet transforms and type recognizer are utilized on this basis to recognize three different common digital signals effectively.The simulation results show that the accuracy could reach 99.4%with OdB.So the algorithm is very effective even when the SNR is low.