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Radio Frequency Signal Identification Using Transfer Learning Based on LSTM

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Abstract

Radio frequency distinct native attribute (RF-DNA) technology is very important in distinguishing RF devices. This paper presents a new method for distinguishing RF devices by combining transfer learning and long short-term memory (TL-LSTM). The main purpose of this paper is to identify which RF device sent the unknown RF signals. The data were collected from almost the same eight RF devices produced in 2011, 2014 or 2016. These RF devices emitted unintended signals at 2.4G bandwidth with frequency shift keying. The proposed method first used late production RF devices in 2011 or 2014 as source domain and transferred the trained model to target domain produced in 2016 and then used neural network LSTM model to identify the RF signals. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi-domain feature space. The results reveal that the proposed method TL-LSTM can solve the problem of small sample training very well.

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References

  1. A. Abbasi, Investigating the effect of traditional persian music on ecg signals in young women using wavelet transform and neural networks. Anatol. J. Cardiol. 17(5), 398–403 (2017)

    Google Scholar 

  2. S. Bai, J. Kolter, V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling (2018)

  3. T.J. Bihl, K.W. Bauer, M.A. Temple, Feature selection for rf fingerprinting with multiple discriminant analysis and using zigbee device emissions. IEEE Trans. Inf. Forensics Secur. 11(8), 1862–1874 (2017)

    Article  Google Scholar 

  4. L. Chen, B. Chen, Y. Ren, D. Ji, Long short-term memory rnn for biomedical named entity recognition. BMC Bioinform. 18(1), 462–472 (2017)

    Article  Google Scholar 

  5. S. Chen, F. Xie, Y. Chen, H. Song, H. Wen, Identification of wireless transceiver devices using radio frequency (rf) fingerprinting based on stft analysis to enhance authentication security, in IEEE International Symposium on Electromagnetic Compatibility, pp. 1–5 (2017)

  6. K. Cho, B.V. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning phrase representations using rnn encoder-decoder for statistical machine translation. Computer Science (2014)

  7. E. Choi, A. Schuetz, W.F. Stewart, J. Sun, Using recurrent neural network models for early detection of heart failure onset. J. Am. Med. Inf. Assoc. Jamia 24(2), 361–370 (2016)

    Google Scholar 

  8. W.E. Cobb, E.D. Laspe, R.O. Baldwin, M.A. Temple, C.K. Yong, Intrinsic physical-layer authentication of integrated circuits. IEEE Trans. Inf. Forensics Secur. 7(1), 14–24 (2012)

    Article  Google Scholar 

  9. D. Garcia-Romero, A. McCree, Stacked long-term tdnn for spoken language recognition, in INTERSPEECH, pp. 3226–3230 (2016)

  10. A. Graves, A.R. Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks, in IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), pp. 6645–6649 (2013)

  11. P.K. Harmer, D.R. Reising, M.A. Temple, Classifier selection for physical layer security augmentation in cognitive radio networks, in IEEE International Conference on Communications, pp. 2846–2851 (2013)

  12. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  13. C.J. Holder, T.P. Breckon, X. Wei, From on-road to off: Transfer learning within a deep convolutional neural network for segmentation and classification of off-road scenes, in Computer Vision – ECCV 2016 Workshops, pp. 149–162. Springer International Publishing (2016)

  14. N. Hu, Y.D. Yao, Identification of legacy radios in a cognitive radio network using a radio frequency fingerprinting based method, in IEEE International Conference on Communications, pp. 1597–1602 (2012)

  15. F. Karim, S. Majumdar, H. Darabi, S. Chen, Lstm fully convolutional networks for time series classification. IEEE Access 6(99), 1662–1669 (2018)

    Article  Google Scholar 

  16. R.W. Klein, M.A. Temple, M.J. Mendenhall, Application of wavelet-based rf fingerprinting to enhance wireless network security. J. Commun. Netw. 11(6), 544–555 (2012)

    Article  Google Scholar 

  17. K. Krishnan, N. Prabhu, R.V. Babu, Arrnet: Action recognition through recurrent neural networks, in International Conference on Signal Processing and Communications, pp. 1–5 (2016)

  18. H. Li, H. Liang, L. Cao, L. Cao, X. Feng, C. Tang, E. Li, Novel ecg signal classification based on kica nonlinear feature extraction. Circuits Syst. Signal Process. 35(4), 1187–1197 (2016)

    Article  MathSciNet  Google Scholar 

  19. H. Li, H. Liang, C. Miao, L. Cao, X. Feng, C. Tang, E. Li, Novel ecg signal classification based on kica nonlinear feature extraction. Circuits Syst. Signal Process. 35(4), 1187–1197 (2016)

    Article  MathSciNet  Google Scholar 

  20. H. Li, N.A. Parikh, L. He, A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front. Neurosci. 12, 491–502 (2018)

    Article  Google Scholar 

  21. P. Liu, X. Qiu, X. Huang, Recurrent neural network for text classification with multi-task learning, in International Joint Conference on Artificial Intelligence, pp. 2873–2879 (2016)

  22. M. Lukacs, P. Collins, M. Temple, Classification performance using ’rf-dna’ fingerprinting of ultra-wideband noise waveforms. Electron. Lett. 51(10), 787–789 (2015)

    Article  Google Scholar 

  23. H. Meng, N. Bianchi-Berthouze, Y. Deng, J. Cheng, J.P. Cosmas, Time-delay neural network for continuous emotional dimension prediction from facial expression sequences. IEEE Trans. Cybernet. 46(4), 916–929 (2017)

    Article  Google Scholar 

  24. H.J. Patel, M.A. Temple, R.O. Baldwin, Improving zigbee device network authentication using ensemble decision tree classifiers with radio frequency distinct native attribute fingerprinting. IEEE Trans. Reliab. 64(1), 221–233 (2015)

    Article  Google Scholar 

  25. H.T.H. Phan, A. Kumar, J. Kim, D. Feng, Transfer learning of a convolutional neural network for hep-2 cell image classification, in IEEE International Symposium on Biomedical Imaging (2016)

  26. S.U. Rehman, K.W. Sowerby, S. Alam, I. Ardekani, Radio frequency fingerprinting and its challenges, in Communications and Network Security, pp. 496–497 (2014)

  27. D.R. Reising, M.A. Temple, Wimax mobile subscriber verification using gabor-based rf-dna fingerprints, in IEEE International Conference on Communications, pp. 1005–1010 (2012)

  28. D.R. Reising, M.A. Temple, J.A. Jackson, Authorized and rogue device discrimination using dimensionally reduced rf-dna fingerprints. IEEE Trans. Inf. Forensics Secur. 10(6), 1180–1192 (2015)

    Article  Google Scholar 

  29. D.R. Reising, M.A. Temple, M.J. Mendenhall, Improving intra-cellular security using air monitoring with rf fingerprints, in Wireless Communications & Networking Conference, pp. 1–6 (2010)

  30. P. Scanlon, I.O. Kennedy, Y. Liu, Feature extraction approaches to rf fingerprinting for device identification in femtocells. Bell Labs Tech. J. 15(3), 141–151 (2010)

    Article  Google Scholar 

  31. M. Schuster, K.K. Paliwal, Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)

    Article  Google Scholar 

  32. T.H. Tsai, L.J. Kau, K.M. Chao, A takagi-sugeno fuzzy neural network-based algorithm with single-channel eeg signal for the discrimination between light and deep sleep stages, in Biomedical Circuits and Systems Conference, pp. 532–535 (2017)

  33. O. Ureten, N. Serinken, Wireless security through rf fingerprinting. Can. J. Electr. Comput. Eng. 32(1), 27–33 (2007)

    Article  Google Scholar 

  34. J. Wang, Y. Yang, J. Mao, Z. Huang, C. Huang, W. Xu, Cnn-rnn: A unified framework for multi-label image classification, in Computer Vision & Pattern Recognition (2016)

  35. X. Wang, S. Takaki, J. Yamagishi, A simple rnn-plus-highway network for statistical parametric speech synthesis. NII Technical Reports

  36. L. Wei, E. Keogh, Semi-supervised time series classification, in Discovery & Data Mining pp. 748–753 (2006)

  37. M.D. Williams, M.A. Temple, D.R. Reising, Augmenting bit-level network security using physical layer rf-dna fingerprinting, in Global Telecommunications Conference, pp. 1–6 (2010)

  38. H. Wu, S. Prasad, Convolutional recurrent neural networks for hyperspectral data classification. Remote Sens. 9(3), 298 (2017)

    Article  Google Scholar 

  39. X. Xia, R. Togneri, F. Sohel, Y. Zhao, D. Huang, A survey: neural network-based deep learning for acoustic event detection. Circuits Syst. Signal Process. 38, 3433–3453 (2019)

    Article  Google Scholar 

  40. Q. Xu, R. Zheng, W. Saad, Z. Han, Device fingerprinting in wireless networks: challenges and opportunities. IEEE Commun. Surv. Tutor. 18(1), 94–104 (2016)

    Article  Google Scholar 

  41. Z. Yu, D.S. Moirangthem, M. Lee, Continuous timescale long-short term memory neural network for human intent understanding. Front. Neurorobot. 11, 42–55 (2017)

    Article  Google Scholar 

  42. R. Zazo, A. Lozano-Diez, J. Gonzalez-Dominguez, T.T. D, J. Gonzalez-Rodriguez, Language identification in short utterances using long short-term memory (lstm) recurrent neural networks. Plos One 11(1), e0146917 (2016)

  43. T. Zhang, W. Zheng, Z. Cui, Y. Zong, Y. Li, Spatial-temporal recurrent neural network for emotion recognition. IEEE Trans. Cybernet. 99, 1–9 (2017)

    Article  Google Scholar 

  44. R. Zhao, R. Yan, J. Wang, K. Mao, Learning to monitor machine health with convolutional bi-directional lstm networks. Sensors 17(2), 273–290 (2017)

    Article  Google Scholar 

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Acknowledgements

We thank the Editor-in-Chief and the Associate Editor for their illuminating suggestions very much. We are also indebted to two reviewers for their valuable comments which greatly improve the performance of the article. This research is supported by the National Key Research and Development Program of China (2019YFC1606003, 2018YFC1603305), Action Plan Project of Beijing University of Posts and Telecommunications (No. 2019XD-A17) and Beijing Natural Science Foundation (No. 3182028).

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Authors and Affiliations

  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, People’s Republic of China

    Xueli Wang, Yufeng Zhang & Yixuan Li

  2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, People’s Republic of China

    Hongxin Zhang & Xiaofeng Wei

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  1. Xueli Wang
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Wang, X., Zhang, Y., Zhang, H. et al. Radio Frequency Signal Identification Using Transfer Learning Based on LSTM. Circuits Syst Signal Process 39, 5514–5528 (2020). https://doi.org/10.1007/s00034-020-01417-7

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