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Multi-label Deep Forest: Towards Automatic Modulation Recognition of Compound Wireless Signals at Low-SNR Environment

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Abstract

As an essential step between signal detection and demodulation, automatic modulation recognition (AMR) technology has been widely applied in commercial and military communication systems. However, due to the rapid development of new technology and increased proliferation of devices, the spectrum environment becomes ever more complex and crowded than before. Therefore, a receiving antenna may simultaneously receive multiple signals from different sources, forming a compound signal. Recently, the AMR problem of compound wireless signal has attracted wide attention in the signal processing domain. To address this, we proposed a multi-label deep forest (MLDF) framework trained on raw compound complex time-domain signals and compared it to four multi-label models, including two multi-label convolutional neural network, residual networks and convolutional long short-term deep neural network. The compared performance of the proposed method against other models demonstrates its superiority under low-SNR training conditions. In addition, to validate the robustness of MLDF trained on small-scale datasets, we designed experiments to reduce the number of training samples. Experiments show that the MLDF trained with only one-fifth of the samples can achieve over 95% performance of using the original dataset. The results show that MLDF has great potential in practical applications when sufficient training data are difficult to obtain, and the training cost is high or training speed is required.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the National Nature Science Foundation of China under Grant 12071024.

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Author notes

  1. L. Liu and X. Wang contributed equally to this work.

Authors and Affiliations

  1. School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China

    Liwen Liu, Yongqi Hu, Chen Wang & Yan Xu

  2. Institute of Applied Physics and Computational Mathematics, Beijing, China

    Xiaobo Wang

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  1. Liwen Liu
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Correspondence to Yan Xu.

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Liu, L., Wang, X., Hu, Y. et al. Multi-label Deep Forest: Towards Automatic Modulation Recognition of Compound Wireless Signals at Low-SNR Environment. Circuits Syst Signal Process 42, 3008–3037 (2023). https://doi.org/10.1007/s00034-022-02257-3

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