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Modulation Recognition Based on Denoising Bidirectional Recurrent Neural Network

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Abstract

Modulation recognition is an important research area in wireless communication. It is commonly used in both military and civilian domains, such as spectrum detection and interference identification. Most existing modulation recognition algorithms have a better recognition performance at high signal noise ratio (SNR). However, when SNR decreases to − 10 dB or even lower, such as the battlefield and disaster areas and other harsh environment, the recognition rate may decrease dramatically. In order to solve this problem, a modulation recognition algorithm based on denoising bidirectional recurrent neural network is proposed. Firstly, the state memory ability of the signal reconstruction layer in the network is utilized to learn the temporal correlation of the modulated signal, the reconstruction of the received signal is completed and the noise in the modulated signal is suppressed. Then, the reconstructed signal is encoded and decoded by the feature reconstruction layer, in which the feature of reconstructed signal is compressed and reconstructed, thereby the influence of noise can be further reduced. Finally, the reconstructed features are identified and classified by the fully connected layer. Simulation results demonstrate that the proposed network can effectively suppress the noise in the signal. Compared with other existing algorithms, the proposed method has higher recognition accuracy in the low SNR environment.

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Data Availability

The data and material generated during and analysed during the current study are available from the corresponding author on reasonable request.

Code Availability

The code generated during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China under Grant No. 61971117, by the Natural Science Foundation of Hebei Province (Grant No. F2020501007).

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Author notes

  1. Ruiyan Du and Lijie Zhang have contributed equally to this work.

Authors and Affiliations

  1. Engineer Optimization and Smart Antenna Institute, Northeastern University at Qinhuangdao, Qinhuangdao, China

    Ruiyan Du & Fulai Liu

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Ruiyan Du, Fulai Liu, Lijie Zhang, Yahui Ji, Jialiang Xu & Fan Gao

Authors
  1. Ruiyan Du
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  2. Fulai Liu
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  3. Lijie Zhang
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  4. Yahui Ji
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  5. Jialiang Xu
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  6. Fan Gao
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Contributions

RD: Conceptualization, Validation, Resources, Writing—review & editing, Supervision. FL: Methodology, Validation, Resources, Writing—original draft, Supervision. LZ: Software, Validation, Resources, Writing—review & editing. YJ: Writing—review & editing, Supervision. JX: Writing—review & editing. FG: Methodology.

Corresponding authors

Correspondence to Fulai Liu or Lijie Zhang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Du, R., Liu, F., Zhang, L. et al. Modulation Recognition Based on Denoising Bidirectional Recurrent Neural Network. Wireless Pers Commun 132, 2437–2455 (2023). https://doi.org/10.1007/s11277-023-10725-5

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  • DOI: https://doi.org/10.1007/s11277-023-10725-5

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