Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification
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ORIGINAL ARTICLE
Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification Qinghe Zheng1
•
Penghui Zhao1
•
Yang Li3,4
•
Hongjun Wang1,2
•
Yang Yang1
Received: 15 June 2020 / Accepted: 4 November 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-ofthe-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. Keywords Automatic modulation classification (AMC) Deep learning Data augmentation Spectrum interference
1 Introduction Nowadays, wireless communication has played an indispensable role in people’s work and lives. Data transmission rate is one of the most important technical indicators of & Hongjun Wang [email protected] & Yang Yang [email protected] 1
School of Information Science and Engineering, Shandong University, Qingdao 266237, China
2
Public (Innovation) Experimental Teaching Center, Shandong University, Qingdao 266237, China
3
School of Information Science and Engineering, University of Jinan, Jinan 250022, China
4
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan 250022, China
wireless communication systems. To achieve efficient data transmission in the complex radio environment, transmitted signals are usually modulated by using various modulation schemes. As an intermediate procedure between
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