Blind ML detection of CPM signals via the EMV algorithm

We propose a blind maximum-likelihood (ML) detection algorithm for continuous-phase modulated (CPM) signals transmitted over a noisy linear FIR channel. This is referred to as the Expectation-Maximization-Viterbi algorithm (EMVA) [1, 2]. The EMVA is a blind algorithm capable of simultaneously performing system identification and signal estimation whenever the transmission system can be modeled as a finite-state machine with unknown parameters, a scenario frequently encountered in signal processing for communications. We specifically focus on CPM, but t he algorithm is equally applicable to any other modulation type, linear or nonlinear. The channel estimate obtained via the EMVA is shown via simulations to, asymptotically (as the SNR increases) attain the most optimistic Cramér-Rao bo und and to have an error performance close to that of the true channel; a 0.5 dB difference is seen at BER equal to 10⁻⁴.