On the error rate evaluation and modulation classification for coherent and noncoherent PSK signals using the transformation of random variable

A modulation type classifier based on statistical moments has been successfully employed to classify multiple phase shift keying (MPSK) signals. The optimum threshold is derived, and the average bit error probability for coherent and noncoherent binary phase shift keying (BPSK) systems using the new probability density function obtained from the transformation of random variables is calculated. A moments-based classifier to classify PSK signals by employing the derived moments of coherently and noncoherently demodulated signals is reported. Performance evaluation of the classifier for BPSK and quadrature phase shift keying (QPSK) signals is investigated in an additive white Gaussian noise (AWGN) environment using the exact distribution of the demodulated signal