Modulation-Constrained Clustering Approach to Blind Modulation Classification for MIMO Systems

Abstract

Blind modulation classification is a fundamental step before signal detection in cognitive radio networks where the knowledge of modulation scheme is not completely known. In this paper, a modulation-constrained (MC) clustering classifier is proposed for recognizing the modulation scheme with unknown channel matrix and noise variance for MIMO systems. By recognizing the fact that the received signals within an observation interval form into clusters and exploiting the intrinsic relationships between different digital modulation schemes, the modulation classification is transformed into a number of clustering problems, one for each modulation scheme, with the final classification decision based on the maximum likelihood criterion. To improve the learning efficiency, centroid reconstruction is proposed to recover all cluster centroids through a limited number of parameters by exploiting the structural relationships in constellation diagrams. Furthermore, a method to initialize the cluster centroids is also proposed. The proposed MC classifier together with centroid reconstruction and initialization methods not only reduce the number of parameters to be estimated, but also help to initialize the clustering algorithm for the enhanced convergence performance. Simulation results show that our algorithm can perform excellently even at low SNR and with very short observation interval length.