${k}$ -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 z⃗ employing K log(K) comparison operations. Here, we propose a k-sparse autoencoder-based classifier, with unsorted input data features and called it unsorted deep neural network (UDNN). Thus, we strive to omit the K log(K) comparison operations. The results obtained using the UDNN classifier show improved performance when compared with the above three methods. Moreover, using k highest hidden units to reconstruct input data further reduces the overall complexity of the AMC system.