Performance Analysis of Modulation Recognition in Multipath Fading Channels using Pattern Recognition Classifiers

Implementation of adaptive modulation techniques based on the channel conditions play a key role in future wireless applications. Due to these adaptive techniques, the performance of the receiver depends on the ability of Blind Modulation Recognition (BMR). This paper investigates the performance of different Pattern Recognition Classifiers (PRC) in modulation recognition under multipath fading channels. Investigations carried out on various non-parametric supervised classifiers which include Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Ensemble Classifiers. To train the classifiers initially a set of 39 features is extracted and then a set of 11 noise robust features is chosen based on investigations through rough set theory. Performance of proposed PRC's is compared with other classifiers stated in literature to prove their superiority in modulation classification under fading conditions.