Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters

Abstract

A generalized modulation identification scheme is developed and presented. With the help of this scheme, the automatic modulation classification and recognition of wireless communication signals with a priori unknown parameters are possible effectively. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to identify. The developed scheme based on wavelet transform and statistical parameters has been used to identify M-ary PSK, M-ary QAM, GMSK, and M-ary FSK modulations. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB. The identification percentage has been analyzed based on the confusion matrix. When SNR is above 5 dB, the probability of detection of the proposed system is more than 0.968. The performance of the proposed scheme has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.

Grey systems theory applications to wireless communications

“…The performance of the GRA-AMC is better if the ROC curve falls faster. ROC plots often have a curve that rises steeply and moves towards P fp = 1 and P CC = 1 [29]. However, this is a characteristic of a receiver that indicates the presence or absence of a signal.…”

“…For an ideal classifier all results, each 100%, are contained in the diagonal matrix elements [29]. Figures 8 and 9 compare the P cc of each modulation against the probability that the GRA modulation choice is a false positive and another modulation was actually transmitted (P fp ).…”

A New Method for MQAM Signals Identification

“…As QAM signal amplitude is variable, the magnitude of wavelet transform shows a multi-level function. When the Haar wavelet covers the symbol change, the magnitude of wavelet transform will be a very large peak [5]. Here we make the magnitude pass through a median filter to filter the peak and reduce the noise [6].…”

Constellation Classification Based on Sequential Monte Carlo for Intersymbol Interference Channels

“…Typical suboptimal methods of classification consist of computing appropriate features from the observed data and applying standard classification rules (such as the nearest-mean of the k-nearest neighbor rule) on these features. The features, which have been used for classification of digital modulation, include moments of the extracted phase [3], estimates of the instantaneous amplitude, phase and frequency [4], high-order cumulants [5][6][7], discrete Fourier transform of the phase histogram [8][9][10], and wavelet coefficients [11][12][13].…”