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Towards recurrent neural network with multi-path feature fusion for signal modulation recognition

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Abstract

Deep learning (DL) technology is an effective tool for automatic modulation recognition (AMR) in the field of cognitive radio (CR). Most of the existing DL-based approaches usually design a deep network with many layers, in which only refined features from the final layer are used for AMR. However, rough features from other layers (i.e., features captured in a shallow network with fewer layers) can also provide useful information for modulation recognition. These rough features are not carefully exploited in previous approaches. In this paper, we propose a novel multi-path features fusion network for AMR, in which both refined and rough features are learned. The proposed approach identifies 11 signals including digital modulation and analog modulation generated by the GNU radio and compared to the classic network in all SNR. The experiment results show that the effectiveness of our approach. Especially, our approach is able to achieve 99.04% accuracy in +18dB SNR which outperforms all comparison approaches.

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References

  1. Abramson, N., Braverman, D. J., & Sebestyen, G. S. (2006). Pattern recognition and machine learning. Publications of the American Statistical Association, 103(4), 886–887.

    Google Scholar 

  2. Aslam, M. W., Zhu, Z., & Nandi, A. K. (2012). Automatic modulation classification using combination of genetic programming and knn. IEEE Transactions on Wireless Communications, 11(8), 2742–2750.

    Google Scholar 

  3. Zhu, Z., & Nandi, A. K. (2014). Machine Learning for Modulation Classification. New York: Wiley.

    Book  Google Scholar 

  4. Zhang, X., Chen, J., & Sun, Z. (2017). Modulation recognition of communication signals based on schks-ssvm. Journal of Systems Engineering and Electronics, 28, 627.

    Article  Google Scholar 

  5. Xie, L., & Wan, Q. (2017). Cyclic feature-based modulation recognition using compressive sensing. IEEE Wireless Communications Letters, 6(3), 402–405.

    Article  Google Scholar 

  6. Park, C. S., Choi, J. H., & Nah, S. P. (2008). Automatic modulation recognition of digital signals using wavelet features and svm. In: 10th international conference on advanced communication technology, 2008. ICACT 2008.

  7. Oshea, T., & Hoydis, J. (2017). An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications& Networking, 3(4), 563–575.

    Article  Google Scholar 

  8. Aceto, G., Ciuonzo, D., Montieri, A., & Pescape, A. (2019). Mobile encrypted traffic classification using deep learning: Experimental evaluation, lessons learned, and challenges. IEEE Transactions on Network and Service Management, 16, 445–458.

    Article  Google Scholar 

  9. Lin, Y., Tu, Y., Dou, Z., & Wu, Z. (2017). The application of deep learning in communication signal modulation recognition. In: 2017 IEEE/CIC International Conference on Communications in China (ICCC), pp. 1–5. IEEE.

  10. Zhang, T., Shuai, C., & Zhou, Y. (2020). Deep learning for robust automatic modulation recognition method for iot applications. IEEE Access, 99, 1.

    Google Scholar 

  11. Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3d convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 221–231. https://doi.org/10.1109/TPAMI.2012.59.

    Article  Google Scholar 

  12. Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing, 26(7), 3142–3155. https://doi.org/10.1109/TIP.2017.2662206.

    Article  MathSciNet  MATH  Google Scholar 

  13. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Transactions on Medical Imaging, 35(5), 1207–1216. https://doi.org/10.1109/TMI.2016.2535865.

    Article  Google Scholar 

  14. Wang, Y., Liu, M., Yang, J., & Gui, G. (2019). Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2019.2900460.

    Article  Google Scholar 

  15. Zhang, M., Diao, M., & Guo, L. (2017). Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access, 5, 11074–11082. https://doi.org/10.1109/ACCESS.2017.2716191.

    Article  Google Scholar 

  16. Liu, X., Yang, D., & El Gamal, A. (2017). Deep neural network architectures for modulation classification. In: 2017 51st Asilomar Conference on Signals, Systems, and Computers, pp. 915–919. IEEE.

  17. Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015). Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4580–4584. https://doi.org/10.1109/ICASSP.2015.7178838

  18. West, N. E., & O’Shea, T. (2017). Deep architectures for modulation recognition. In: 2017 IEEE international symposium on dynamic spectrum access networks (DySPAN) (pp. 1–6 ). https://doi.org/10.1109/DySPAN.2017.7920754

  19. O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016). Convolutional radio modulation recognition networks. In: International conference on engineering applications of neural networks, pp. 213–226. Berlin: Springer

  20. Yu, W., Yang, K., Yao, H., Sun, X., & Xu, P. (2016). Exploiting the complementary strengths of multi-layer cnn features for image retrieval. Neurocomputing. https://doi.org/10.1016/j.neucom.2016.12.002.

    Article  Google Scholar 

  21. Guo, C., Wang, H., Jian, T., He, Y., & Zhang, X. (2019). Radar target recognition based on feature pyramid fusion lightweight cnn. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2909348.

    Article  Google Scholar 

  22. Huang, S., Liy, X., Jiang, Z., Guo, X., & Men, A. (2018). Fully convolutional network with densely feature fusion models for object detection. In: 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6. IEEE.

  23. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.

    Article  Google Scholar 

  24. Nandi, A. K., & Azzouz, E. E. (1998). Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on Communications, 46(4), 431–436.

    Article  Google Scholar 

  25. Roganovic, M. M., Neskovic, A. M., & Neskovic, N. J. (2009). Application of artificial neural networks in classification of digital modulations for software defined radio. In: IEEE EUROCON 2009, pp. 1700–1706. IEEE.

  26. Zhu, X., Lin, Y., & Dou, Z. (2016). Automatic recognition of communication signal modulation based on neural network. In: 2016 IEEE international conference on electronic information and communication technology (ICEICT), pp. 223–226 . https://doi.org/10.1109/ICEICT.2016.7879688

  27. Li, J., Qi, L., & Lin, Y. (2016). Research on modulation identification of digital signals based on deep learning. In: 2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT), pp. 402–405 . https://doi.org/10.1109/ICEICT.2016.7879726

  28. Wei, M., Wei, Z., Yang, J., & Sang, L. (2018). Automatic modulation recognition of digital signal based on auto-encoding network in mimo system. In: 2018 IEEE 18th international conference on communication technology (ICCT), pp. 1017–1021 . https://doi.org/10.1109/ICCT.2018.8600148

  29. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791.

    Article  Google Scholar 

  30. Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). Imagenet classification with deep convolutional neural networks. Neural Information Processing Systems. https://doi.org/10.1145/3065386.

    Article  Google Scholar 

  31. Kulin, M., Kazaz, T., Moerman, I., & De Poorter, E. (2017). End-to-end learning from spectrum data a deep learning approach for wireless signal identification in spectrum monitoring applications. IEEE Access, 6, 18484–18501. https://doi.org/10.1109/ACCESS.2018.2818794.

    Article  Google Scholar 

  32. Li, M., Li, O., Liu, G., & Zhang, C. (2018). Generative adversarial networks-based semi-supervised automatic modulation recognition for cognitive radio networks. Sensors, 18(11), 3913.

    Article  Google Scholar 

  33. Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., et al. (2015). Deep sentence embedding using the long short term memory network: Analysis and application to information retrieval. IEEE ACM Transactions on Audio, Speech, and Language Processing.https://doi.org/10.1109/TASLP.2016.2520371.

    Article  Google Scholar 

  34. Sutskever, I., Vinyals, O., & Le, Q. (2014). Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 4.

  35. Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. (2014). Grammar as a foreign language. arXiv preprint arXiv:1412.7449

  36. Lee, Y.H., Moss, D.J., Faraone, J., Blackmore, P., Salmond, D., Boland, D., & Leong, P.H., et al. (2018). Long short-term memory for radio frequency spectral prediction and its real-time FPGA implementation. In: MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), pp. 1–9. IEEE.

  37. Yu, X., Xu, L., Ma, L., Chen, Z., & Yan, Y. (2017). Solar radio spectrum classification with lstm. In: 2017 IEEE international conference on multimedia & expo workshops (ICMEW), pp. 519–524. IEEE.

  38. Sang, Y., Li, L. (2018). Application of novel architectures for modulation recognition. In: 2018 IEEE Asia Pacific conference on circuits and systems (APCCAS), pp. 159–162. IEEE.

  39. Song, L., Qian, X., Li, H., & Chen, Y. (2017). Pipelayer: A pipelined reram-based accelerator for deep learning. In: 2017 IEEE international symposium on high performance computer architecture (HPCA), pp. 541–552. IEEE.

  40. Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp. 448–456. PMLR.

  41. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv

  42. Graves, A., Jaitly, N., & Mohamed, A.R. (2013). Hybrid speech recognition with deep bidirectional lstm. In: 2013 IEEE workshop on automatic speech recognition and understanding, pp. 273–278. IEEE.

  43. 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(1), 168–179.

    Article  Google Scholar 

  44. Courtat, T., Bourboux, & H.d.M.d. (2020). A light neural network for modulation detection under impairments. arXiv preprint. arXiv:2003.12260

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Authors and Affiliations

  1. School of Ocean Information Engineering, Jimei University, Xiamen, 361023, China

    Zihang Lei, Mengxi Jiang, Guangsong Yang, Tianmin Guan, Yu Gu & Qiubo Ye

  2. Xiamen University, Xiamen, 361005, China

    Mengxi Jiang

  3. Ceyear Technology Co. Ltd., Qingdao, China

    Peng Huang

  4. Hebei University of Technology, Tianjin, 300401, China

    Zhenghua Xu

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  1. Zihang Lei
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Correspondence to Qiubo Ye.

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Lei, Z., Jiang, M., Yang, G. et al. Towards recurrent neural network with multi-path feature fusion for signal modulation recognition. Wireless Netw 28, 551–565 (2022). https://doi.org/10.1007/s11276-021-02877-8

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