Data Format Classification for Autonomous Radio Receivers

We present maximum-likelihood (ML) coherent and noncoherent classifiers for discriminating between non-return to zero (NRZ) and Manchester coded data formats for binary phase-shift-keying (BPSK) and quadrature phase-shift-keying (QPSK) modulations. Small and large signal-to-noise ratio (SNR) approximations to the ML classifiers also are proposed that lead to simpler implementation with comparable performance in their respective SNR regions. Both suppressed and residual carrier cases are considered, and various numerical comparisons are made among the various configurations based on the probability of misclassification as a performance criterion.