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A deep learning based algorithm with multi-level feature extraction for automatic modulation recognition

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Abstract

Automatic modulation recognition is a critical challenge in the field of cognitive radio. In the process of communication, radio signals are modulated in various modes and are interfered by the complex electromagnetic environment. To cope with these problems and avoid manual selection of complex expert features, we propose a multi-level feature extraction algorithm based on deep learning to adequately exploit the hidden feature information of modulated signals. Our algorithm integrates the correlation between the channels of radio signals with convolutional neural networks and Bidirectional Long Short-term Memory (Bi-LSTM), and adopts the appropriate skip connection, which avoids the loss of valid information and achieves the complementarity between spatial and temporal features. In our model, the one-dimensional convolutional layer is specially utilized to enrich the feature representation of each sample point of in-phase and quadrature (I/Q) signals and emphasize the mutual influence of I channel (in-phase signal) and Q channel (quadrature signal). In addition, the label smoothing technique is used to improve the generalization ability of the model. Our proposed method is also of certain significance for other signal processing methods based on deep learning. Experiment results demonstrate that our algorithm outperforms the popular algorithms and is of higher robustness. Specifically, the proposed method improves the recognition accuracy, reaching 92.68% at high signal-to-noise ratio (SNR). In particular, it also reduces the difficulty of recognition for multiple quadrature amplitude modulation (MQAM) signals and significantly improves the recognition accuracy for 16QAM and 64QAM.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (2019YFC1510705), the Sichuan Science and Technology Program (2020YFG0051), and the University-Enterprise Cooperation Projects (17H1199, 19H0355, 19H1121). We also thank Michael Tan of University College London for his writing suggestions.

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Authors and Affiliations

  1. College of Electrical Engineering, Sichuan University, 24 South Section 1, One Ring Road, Chengdu, 610065, China

    Hang Zhang, Minghui Lin, Xiaofeng Gong, Qin Yu & Ruisen Luo

  2. State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610065, Sichuan, China

    Ruihua Nie

  3. Chengdu Dagongbochuang Information Technology Co., Ltd, Chengdu, 610059, China

    Ruijuan Wu & Guo Xian

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  1. Hang Zhang
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  2. Ruihua Nie
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Correspondence to Ruisen Luo.

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Zhang, H., Nie, R., Lin, M. et al. A deep learning based algorithm with multi-level feature extraction for automatic modulation recognition. Wireless Netw 27, 4665–4676 (2021). https://doi.org/10.1007/s11276-021-02758-0

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