Parameter estimation for blind classification of digital modulations

In this study, the performance of the likelihood-based digital modulation classification is explored with the blind estimation of the unknown parameters. Considering the practical implementation aspects, the quasi hybrid likelihood ratio test (QHLRT) is examined with the symbol rate, the signal gain, the noise power and the phase offset as the unknown parameters. In a blind scenario, new algorithms are proposed for the estimation of the unknown parameters with special focus on the improvement of classification performance at a low signal to noise ratio scenario. The performance bounds of the proposed estimators are established by the Cramer-Rao lower bound. The proposed method is compared with several existing algorithms to analyse the improvements achieved in the slow fading scenario. With the estimates of the unknown parameters, the performance of the QHLRT classifier is presented with reference to the theoretical upper bound. Finally, the QHLRT based method with the proposed parameter estimators is compared with the existing LB as well as certain feature based algorithms to highlight the improvements achieved.