κ-Sparse Autoencoder-Based Automatic Modulation Classification with Low Complexity

How to reduce complexity of the practical automatic modulation classification systems is a very active research area. Moreover, Keeping the classification accuracy to a near optimal level is an added challenge. Recently, three new classifiers have been proposed with reduced complexity, mainly: linear support vector machine classifier, approximate maximum likelihood classifier, and backpropogation neural networks classifier. However, these methods include the sorting process of the features z to form an ordered vector z employing Klog(K) comparison operations. Here, we propose a κ-sparse autoencoder-based classifer, with unsorted input data features and called it unsorted deep neural network(UDNN). Thus, we strive to omit the Klog(K) comparison operations. The results obtained using the UDNN classifier show improved performance when compared with the above three methods. Moreover, using Khighest hidden units to reconstruct input data further reduces the overall complexity of the AMC system.