Advertisement

Peer-to-Peer Networking and Applications

, Volume 12, Issue 6, pp 1615–1623 | Cite as

Communication modulation recognition algorithm based on STFT mechanism in combination with unsupervised feature-learning network

  • Suqin WuEmail author
Article
  • 77 Downloads
Part of the following topical collections:
  1. Special Issue on Fog/Edge Networking for Multimedia Applications

Abstract

Aiming at the limitations of traditional communication modulation recognition algorithms, a novel recognition algorithm based on deep learning network far communication signal features is proposed in this paper. By introducing the two different computation mechanisms of STFT, two unsupervised feature-learning networks based on Restrict Boltzmann Machine (RBM) are respectively adopted for communication signal, where the network based on RBM is greatly improved in computing performance compared to the network based on convolutional Restrict Boltzmann Machine (CRBM), and greatly reduces the requirement for high-performance hardware in deep learning networks. In addition, as for signal modulation recognition and signal detection problems in communications, two modulation recognition networks are constructed by using the learning-to-STFT networks and the Back Propagation Neural Network (BPNN) classifier. Compared with the traditional modulation algorithms, our proposed algorithm in the paper can obtain better performance in the recognition accuracy, especially under the condition of low SNR.

Keywords

Modulation recognition Deep learning Restrict Boltzmann machine Learning-to-STFT Back propagation STFT time-frequency feature 

Notes

References

  1. 1.
    Karra K, Kuzdeba S, Petersen J. Modulation recognition using hierarchical deep neural networks[C]// IEEE international symposium on dynamic Spectrum access networks. 2017Google Scholar
  2. 2.
    Yuan L, Xinyu DA, Jialiang WU et al (2018) WFRFT modulation recognition based on HOC and optimal order searching algorithm[J]. Systems Engineering and Electronic Technology (English edition)Google Scholar
  3. 3.
    Mohamed B, Hua W, Lakhdari MEH (2018) Automatic digital modulation recognition based on stacked sparse AutoEncoder[C]// IEEE international conference on communication technology. IEEEGoogle Scholar
  4. 4.
    Li R, Li L, Yang S et al (2018) Robust automated VHF modulation recognition based on deep convolutional neural networks[J]. IEEE Commun Lett:1–1Google Scholar
  5. 5.
    Kumar Y, Amit S, Prasanna C A learnable distortion correction module for modulation recognition[J]. IEEE Wireless Commun Lett 2018:1–1Google Scholar
  6. 6.
    Wang H, Guo L, Dou Z, Lin Y (2018) A new method of cognitive signal recognition based on hybrid information entropy and D-S evidence theory[J]. Mobile Networks and Applications 23:677–685CrossRefGoogle Scholar
  7. 7.
    Almohamad TA, Salleh MFM, Mahmud MN et al (2018) Simultaneous determination of modulation types and signal-to-noise ratios using feature-based approach[J]. IEEE Access:1–1Google Scholar
  8. 8.
    Kulin M, Kazaz T, Moerman I, de Poorter E (2018) End-to-end learning from Spectrum data: a deep learning approach for wireless signal identification in Spectrum monitoring applications[J]. IEEE Access 6:18484–18501CrossRefGoogle Scholar
  9. 9.
    Bin Z , Morgan M E , Van d K H J G , et al (2018). Corrigendum Transcriptional modulation of pattern recognition receptors in chronic colitis in mice is accompanied with Th1 and Th17 response[J]. Biochemistry and Biophysics Reports, (13):147–148CrossRefGoogle Scholar
  10. 10.
    Saylor Z, Maier R (2018) Helicobacter pylori nickel storage proteins: recognition and modulation of diverse metabolic targets[J]. Microbiology 164:1059–1068CrossRefGoogle Scholar
  11. 11.
    Wang H, Cui J, Arshad A, Xu S, Wang L (2018) A visual photothermal paper sensor for H2S recognition through rational modulation LSPR wavelength of plasmonics[J]. SCIENCE CHINA Chem 61(3):368–374CrossRefGoogle Scholar
  12. 12.
    Cristina MC, Leonardo S, Silvina CL et al (2018) Helminth infections: recognition and modulation of the immune response by innate immune cells[J]. Front Immunol 9:664CrossRefGoogle Scholar
  13. 13.
    Ren W, Ji D, Xu X et al (2018) Metal cofactor modulated folding and target recognition of HIV-1 NCp7[J]. PLoS One 13(5)CrossRefGoogle Scholar
  14. 14.
    Bijlstra G, Holland RW, Dotsch R et al (2018) Stereotypes and prejudice affect the recognition of emotional body postures[J]. EmotionGoogle Scholar
  15. 15.
    Cheng X, Liu Y, Shu Y, Tao DD, Wang B, Yuan Y, Galvin JJ III, Fu QJ, Chen B (2018) Music training can improve music and speech perception in pediatric mandarin-speaking Cochlear implant users[J]. Trends in Hearing 22:233121651875921CrossRefGoogle Scholar
  16. 16.
    Liu T, Guan Y, Lin Y (2017) Research on modulation recognition with ensemble learning[J]. EURASIP J Wirel Commun Netw 2017(1):179CrossRefGoogle Scholar
  17. 17.
    Spooner CM, Mody AN, Chuang J et al (2017) Modulation recognition using second- and higher-order cyclostationarity[C]// IEEE international symposium on dynamic Spectrum access networks. IEEEGoogle Scholar
  18. 18.
    Chao YW, Quan YX (2017) Modulation recognition of double satellite signals in alpha-stable distribution noise[J]. J Appl Sci 35(3):309–316Google Scholar
  19. 19.
    Xie L, Wan Q (2017) Cyclic feature based modulation recognition using compressive sensing[J]. IEEE Wireless Commun Lett:1–1Google Scholar
  20. 20.
    Zhang Y, Yang M, Liu X (2017) Artificial-neural-network-based automatic modulation recognition in satellite communication[C]// international conference on Machine Learning & Intelligent Communications. Springer, ChamGoogle Scholar
  21. 21.
    Hazar MA, Odabasioglu N, Ensari T et al Performance analysis and improvement of machine learning algorithms for automatic modulation recognition over Rayleigh fading channels[J]. Neural Comput Applic 2017(12):1–10Google Scholar
  22. 22.
    Jin S, Lin Y, Wang H . Automatic modulation recognition of digital signals based on Fisherface[C]// IEEE international conference on software quality. IEEE, 2017Google Scholar
  23. 23.
    Kumar H L P, Shrinivasan L . Automatic digital modulation recognition system using feature extraction[J]. 2017Google Scholar
  24. 24.
    Xie J (2017) Robust intra-pulse modulation recognition via sparse representation[C]// Cie international conference on radar. IEEEGoogle Scholar
  25. 25.
    Khosraviani M, Kalbkhani H, Shayesteh MG (2017) Digital modulation recognition in MIMO systems based on segmentation of received data[C]// electrical engineering. IEEEGoogle Scholar
  26. 26.
    Xie L, Wan Q (2017, PP) Automatic modulation recognition for phase shift keying signals with compressive measurements[J]. IEEE Wireless Commun Lett:99):1–99):1Google Scholar
  27. 27.
    Benedetto F, Tedeschi A, Giunta G (2017) Automatic blind modulation recognition of analog and digital signals in cognitive radios[C]// vehicular technology conference. IEEEGoogle Scholar
  28. 28.
    Xu Y, Mcsally J, Andricioaei I et al (2018) Modulation of Hoogsteen dynamics on DNA recognition[J]. Nat Commun 9(1):1473CrossRefGoogle Scholar
  29. 29.
    Arumugam KSK, Kadampot IA, Tahmasbi M et al (2017) Modulation recognition using side information and hybrid learning[C]// IEEE international symposium on dynamic Spectrum access networks. IEEEGoogle Scholar
  30. 30.
    West N E, O'Shea T J . Deep architectures for modulation recognition[J]. 2017Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information EngineeringYancheng Institute of TechnologyYanchengChina

Personalised recommendations