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.
Highlights
The rapid growth in the field of mobile communication in general, software defined radio (SDR) in particular, has motivated the researchers to develop various digital modulation identification algorithms [1]
Automatic modulation identification (AMI) algorithm reported by Prakasam and Madheswaran [16] has been developed to classify QPSK and GMSK signals with additive white Gaussian noise (AWGN) channel
The wavelet transform has the special feature of multiresolution analysis (MRA), which provides the necessary parameters to extract the feature of the modulated signals
Summary
The rapid growth in the field of mobile communication in general, software defined radio (SDR) in particular, has motivated the researchers to develop various digital modulation identification algorithms [1]. Automatic modulation identification (AMI) algorithm reported by Prakasam and Madheswaran [16] has been developed to classify QPSK and GMSK signals with additive white Gaussian noise (AWGN) channel. This algorithm failed to identify when SNR is less than 12 dB. An attempt is made to propose a generalized modulation identification model to identify BPSK, QPSK, 8PSK, 16PSK, 2QAM, 4QAM, 8QAM, 16QAM, GMSK, and MFSK modulation schemes under noisy environment with low SNR considering both wavelet transform approach as well as statistical moments
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