Classification of linear and nonlinear modulations using the Baum-Welch algorithm

Nonlinear GMSK modulations are becoming new modulation standards for Telemetry/Telecommand satellite links. These new satellite systems will have to co-exist with other space systems using different modulation schemes. In this context, modulation recognition using the received communication signal is useful to identify a possibly perturbing system. This paper studies a Bayesian classifier to recognize BPSK, QPSK, 8PSK and two standardized GMSK modulations in the presence of additive white Gaussian noise. A modified version of the Baum-Welch (BW) algorithm is used to compute the posterior probabilities of the received signal, given each possible model, and to estimate the unknown model parameters. The posterior probabilities are then plugged into the optimal Bayes decision rule. The performance of the proposed classifier is assessed through several simulation results.