Automatic Classification of Imperfect QAM Constellation Using Radon Transform

New automatic classification algorithms are proposed for the imperfect rectangular QAM constellation with phase rotation. Our proposed algorithms are developed based on the two-dimensional Radon transform, and can effectively estimate the phase rotation and classify the modulation type of the received signals. Simulation experiments are performed and the results show that our proposed algorithms are successful even when the incoming signals are corrupted by additive white Gaussian noise (AWGN). As compared with the existing classification algorithm, our proposed algorithms can achieve satisfied performance in terms of probability of correct classification (PCC), and are more feasible to be adopted in practice.