Skip to main content
SpringerLink
Log in

An electromagnetic signal classification method inspired by the visual characteristics of biological dual-channel

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Automatic modulation classification of electromagnetic signals has an important role in the field of civil and military signal processing, its researches are mainly based on deep learning methods. However, existing deep learning models generally have the problems such as network redundancy and non-interpretability. Since the biological visual system has extraordinary visual signal recognition ability, this paper constructs the biological dual-channel visual neural network (BDVNN) to complete the task of signal classification. The input part of BDVNN simulates the dual-channel structure of lateral geniculate nucleus (LGN), building a dual-channel parallel module composed of convolution kernels of different scales to extract the overall coarse features and local fine features from the input signal. The middle part of BDVNN simulates the fusion of coarse and fine features by the primary visual cortex receptive fields, which facilitates the further extraction of advanced features in subsequent layers of the network. On the setting of network layers, BDVNN model refers to the biological visual pathway hierarchy to meet the lightweight characteristics so as to significantly reduce the training cost. For RadioML2016.10a dataset, the recognition accuracy of BDVNN has 2~17% improvement over the rest of mainstream algorithms. For ACARS dataset, the average recognition accuracy of BDVNN for 20 signals is 95.2%, which is also better than other mainstream algorithms. The experimental results show that the proposed model is lightweight and interpretable, while achieving high recognition accuracy, which provides a new idea for the optimization of the structure and function of deep convolutional neural networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The authors declare that RadioML2016.10a data supporting the findings of this study are available within the article [O’Shea, T.J., West, N.: Radio machine learning dataset generation with gnu radio. Proceedings of the GNU Radio Conference 1 (2016).] ACARS data supporting the findings of this study are available within the articlehttps://doi.org/10.1109/ACCESS.2019.2925569.

References

  1. Shah, M. H., & Dang, X. (2019). Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse-autoencoder-based deep neural network. IET Communications, 13, 423–432. https://doi.org/10.1049/iet-com.2018.5688

    Article  Google Scholar 

  2. Chakraborti, T., Jha, D. K., Chowdhury, A. S., et al. (2015). A self-adaptive matched filter for retinal blood vessel detection. Machine Vision and Applications, 26, 55–68. https://doi.org/10.1007/s00138-014-0636-z

    Article  Google Scholar 

  3. Digham, F. F., Alouini, M. S., & Simon, M. K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55, 21–24. https://doi.org/10.1109/ICC.2003.1204119

    Article  Google Scholar 

  4. Tian, Z., Tafesse, Y., & Sadler, B. M. (2011). Cyclic feature detection with sub-Nyquist sampling for wideband spectrum sensing. IEEE Journal of Selected topics in signal processing, 6, 58–69. https://doi.org/10.1109/JSTSP.2011.2181940

    Article  ADS  Google Scholar 

  5. O’Shea, T.J., West, N. (2016). Radio machine learning dataset generation with gnu radio. Proceedings of the GNU Radio Conference 1.

  6. O’Shea, T.J., Corgan, J., Clancy, T.C. (2016). Convolutional radio modulation recognition networks. In: International Conference on Engineering Applications of Neural Networks, Springer, Cham 213-226

  7. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9, 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  CAS  PubMed  Google Scholar 

  8. Kim, H. I., & Park, R. H. (2018). Residual LSTM attention network for object tracking. IEEE Signal Processing Letters, 25, 1029–1033. https://doi.org/10.1109/LSP.2018.2835768

    Article  ADS  Google Scholar 

  9. Roy, D., Mukherjee, T., Chatterjee, M. et al. (2019). Primary user activity prediction in DSA networks using recurrent structures. 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), IEEE 1-10. https://doi.org/10.1109/DySPAN.2019.8935716

  10. Ke, Z., & Vikalo, H. (2012). Real-time radio technology and modulation classification via an LSTM auto-encoder. IEEE Transactions on Wireless Communications, 21, 370–382. https://doi.org/10.1109/TWC.2021.3095855

    Article  Google Scholar 

  11. Chen, K., Zhang, J., Chen, S., et al. (2022). Automatic modulation classification of radar signals utilizing X-net. Digital Signal Processing, 123, 103396. https://doi.org/10.1016/j.dsp.2022.103396

    Article  Google Scholar 

  12. Shi, F., Hu, Z., Yue, C., et al. (2022). Combining neural networks for modulation recognition. Digital Signal Processing, 120, 103264. https://doi.org/10.1016/j.dsp.2021.103264

    Article  Google Scholar 

  13. Peng, S., Jiang, H., Wang, H., et al. (2018). Modulation classification based on signal constellation diagrams and deep learning. IEEE Transactions on Neural Networks and Learning Systems, 30, 718–727. https://doi.org/10.1109/TNNLS.2018.2850703

    Article  PubMed  Google Scholar 

  14. Wu, H., Li, Y., Zhou, L., et al. (2019). Convolutional neural network and multi-feature fusion for automatic modulation classification. Electronics Letters, 55, 895–897. https://doi.org/10.1049/el.2019.1789

    Article  ADS  Google Scholar 

  15. Lin, Y., Tu, Y., Dou, Z., Chen, L., & Mao, S. (2020). Contour stella image and deep learning for signal recognition in the physical layer. IEEE Transactions on Cognitive Communications and Networking, 7(1), 34–46. https://doi.org/10.1109/TCCN.2020.3024610

    Article  Google Scholar 

  16. Zhao, J., Mao, X., & Chen, L. (2019). Speech emotion recognition using deep 1D & 2D CNN LSTM networks. Biomedical Signal Processing and Control, 47, 312–323. https://doi.org/10.1016/j.bspc.2018.08.035

    Article  Google Scholar 

  17. Ali, A., & Fan, Y. Y. (2017). Automatic modulation classification using deep learning based on sparse autoencoders with nonnegativity constraints. IEEE Signal Processing letters, 24, 1626–1630. https://doi.org/10.1109/LSP.2017.2752459

    Article  ADS  Google Scholar 

  18. Wang, Y., Gui, G., Ohtsuki, T., et al. (2021). Multi-task learning for generalized automatic modulation classification under non-Gaussian noise with varying SNR conditions. IEEE Transactions on Wireless Communications, 20, 3587–3596. https://doi.org/10.1109/TWC.2021.3052222

    Article  Google Scholar 

  19. Kim, B., Sagduyu, Y. E., Davaslioglu, K., et al. (2021). Channel-aware adversarial attacks against deep learning-based wireless signal classifiers. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2021.3124855

    Article  Google Scholar 

  20. Palmerston, J. B., Zhou, Y., & Chan, R. H. (2020). Comparing biological and artificial vision systems: Network measures of functional connectivity. Neuroscience Letters, 739, 135407. https://doi.org/10.1016/j.neulet.2020.135407

    Article  CAS  PubMed  Google Scholar 

  21. Lv, Z., Qiao, L., Singh, A. K., et al. (2021). Fine-grained visual computing based on deep learning. ACM Transactions on Multimidia Computing Communications and Applications, 17, 1–19. https://doi.org/10.1145/3418215

    Article  CAS  Google Scholar 

  22. Wei, B., He, H., Hao, K., et al. (2020). Visual interaction networks: A novel bio-inspired computational model for image classification. Neural Networks, 130, 100–110. https://doi.org/10.1016/j.neunet.2020.06.019

    Article  PubMed  Google Scholar 

  23. Haut, J. M., Paoletti, M. E., Plaza, J., et al. (2019). Visual attention-driven hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57, 8065–8080. https://doi.org/10.1109/TGRS.2019.2918080

    Article  ADS  Google Scholar 

  24. Chen, S., & Houde, C. (2015). Pulvinar involves in multiple pathways of emotion processing. Advances in Psychological Science, 23, 234–240. https://doi.org/10.3724/SP.J.1042.2015.00234

    Article  Google Scholar 

  25. Kaplan, E. (2004). The M, P and K pathways of the primate visual system. The Visual Neurosciences, 1, 481–493.

    Google Scholar 

  26. Evans, H. M. (2000). The emotional brain: The mysterious underpinnings of emotional life. Neuropsychoanalysis, 2, 91–95. https://doi.org/10.1080/15294145.2000.10773288

    Article  Google Scholar 

  27. Dicarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition? Neuron, 73, 415–434. https://doi.org/10.1016/j.neuron.2012.01.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rajendran, S., Meert, W., Giustiniano, D., et al. (2018). Deep learning models for wireless signal classification with distributed low-cost spectrum sensors. IEEE Transactions on Cognitive Communications and Networking, 4, 433–445. https://doi.org/10.1109/TCCN.2018.2835460

    Article  Google Scholar 

  29. Chen, S., Zheng, S., Yang, L., et al. (2019). Deep learning for large-scale real-world ACARS and ADS-B radio signal classification. IEEE Access, 7, 89256–89264. https://doi.org/10.1109/ACCESS.2019.2925569

    Article  Google Scholar 

  30. Xu, J., Luo, C., Parr, G., et al. (2020). A spatiotemporal multi-channel learning framework for automatic modulation recognition. IEEE Wireless Communications Letters, 9, 1629–1632. https://doi.org/10.1109/LWC.2020.2999453

    Article  Google Scholar 

  31. O’Shea, T. J., Roy, T., & Clancy, T. C. (2018). Over-the-air deep learning based radio signal classification. IEEE Journal of Selected Topics in Signal Processing, 12, 168–179. https://doi.org/10.1109/JSTSP.2018.2797022

    Article  ADS  Google Scholar 

  32. Sainath, T.N., Vinyals, O., Senior, A. et al. (2015). Convolutional, long short-term memory, fully connected deep neural networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE 4580-4584. https://doi.org/10.1109/ICASSP.2015.7178838

  33. Tekbıyık, K., Ekti, A.R., Görçin, A. et al. (2020). Robust and fast automatic modulation classification with CNN under multipath fading channels. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), IEEE 1-6. https://doi.org/10.1109/VTC2020-Spring48590.2020.9128408

Download references

Funding

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.

Author information

Authors and Affiliations

  1. Science and Technology on Communication Information Security Control Laboratory, Jiaxing, 314000, China

    Shaogang Dai & Huaji Zhou

  2. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Shaogang Dai

Authors
  1. Shaogang Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huaji Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huaji Zhou.

Ethics declarations

Conflict of interest

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.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, S., Zhou, H. An electromagnetic signal classification method inspired by the visual characteristics of biological dual-channel. Wireless Netw 30, 909–921 (2024). https://doi.org/10.1007/s11276-023-03518-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03518-y

Keywords

Search

Navigation