In this paper we propose a modulation classification scheme based on deep learning and convolutional neural networks. The proposed solution is based on I/Q amplitude samples that are classified into a set of 24 modulations, including analog and digital modulations. We investigate the adoption of different features in the training data. The features are computed using I/Q amplitude samples and include statistical information obtained in the time-domain and frequency-domain. Given the high amount of features considered in the training data, we investigate the possibility of projecting the features’ data into a set of uncorrelated variables through the Principal Component Analysis procedure. We evaluate the classification performance for the different modulations and signal-to-noise ratio (SNR) values. Moreover, we also quantity the performance of the classifier when the features’ data is projected into a subset of the uncorrelated variables. Finally, we compare the performance of the classifier with other works already available in the literature, assessing the effective performance gains of the proposed solution.
Modulation Classification using Joint Time and Frequency-domain Data
2020-05-01
178827 byte
Conference paper
Electronic Resource
English
Helicopter classification using time-frequency analysis
IET Digital Library Archive | 2000
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