期刊
IEEE COMMUNICATIONS LETTERS
卷 21, 期 10, 页码 2162-2165出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2017.2717821
关键词
Automatic modulation classification; deep neural network; k-sparse autoencoders
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) over right arrow employing Klog(K) comparison operations. Here, we propose a k-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 k highest hidden units to reconstruct input data further reduces the overall complexity of the AMC system.
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