Intelligent Denoising-Aided Deep Learning Modulation Recognition with Cyclic Spectrum Features for Higher Accuracy

Document Type

Article

Publication Date

12-1-2021

Identifier/URL

40904083 (Pure)

Abstract

Deep-learning-based modulation recognition methods can extract the features of signals automatically with the usage of the deep neural network (DNN). However, the background noises might lower the recognition accuracy and induce longer convergence time. In order to improve the recognition accuracy and to reduce the computational complexity, in this article, we propose to construct the dataset based on spectral correlation function (SCF), which has the special property of relatively being insensitive to background noises. Moreover, we propose to add a convolutional neural network denoising module to combat the noises for better feature extraction performances. Furthermore, in order to reduce the dataset size, we propose to use the two-dimensional (2-D) cross-section SCF profiles for the DNN. Then, we propose a DNN architecture based on the back-propagation neural network. Thanks to the noise resistant SCF feature extractions and the intelligent denoising as well as DNN-based classification design, the recognition accuracy can be improved. Simulation results show that the proposed method can effectively classify modulation schemes at the signal-to-noise ratio (SNR) as low as −10 dB. Moreover, the proposed intelligent system achieves higher recognition accuracy than counterpart schemes in low-SNR regions.

DOI

10.1109/TAES.2021.3083406

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