Skip to main content
SpringerLink
Log in

Modulation Recognition with Alpha-Stable Noise Over Fading Channels

  • Conference paper
  • First Online:
6GN for Future Wireless Networks (6GN 2020)

Included in the following conference series:

Abstract

This paper proposes a method based on kernel density estimation (KDE) and expectation condition maximization (ECM) to realize digital modulation recognition over fading channels with non-Gaussian noise in the cognitive radio networks. A compound hypothesis test model is adopt here. The KDE method is used to estimate the probability density function of non-Gaussian noise, and the improved ECM algorithm is used to estimate the fading channel parameters. Numerical results show that the proposed method is robust to the noise type over fading channels. Moreover, when the GSNR is 10 dB, the correct recognition rate for the digital modulation recognition under non-Gaussian noise is more than 90%. Gaussian noise, and the improved ECM algorithm is used to estimate the fading channel parameters. Numerical results show that the proposed method is robust to the noise type over fading channels. Moreover, when the GSNR is 10 dB, the correct recognition rate for the digital modulation recognition under non-Gaussian noise is more than 90%.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, D.Y., Zhang, N., Li, Z., et al.: Leveraging high order cumulants for spectrum sensing and power recognition in cognitive radio networks. IEEE Trans. Wireless Commun. 17(2), 1298–1310 (2018)

    Article  Google Scholar 

  2. Zhang, M., Diao, M., Guo, L.M.: Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access 5, 11074–11082 (2017)

    Article  Google Scholar 

  3. Yang, G.S., Wang, J., Zhang, G.Y., et al.: Joint estimation of timing and carrier phase offsets for MSK signals in alpha-stable noise. IEEE Commun. Lett. 22(1), 89–92 (2018)

    Article  Google Scholar 

  4. Ma, J., Lin, S., Gao, H., Qiu, T.: Automatic modulation classification under non-gaussian noise: a deep residual learning approach. In: ICC2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, pp. 1-6 (2019)

    Google Scholar 

  5. Dutta, T., Satija, U., Ramkumar, B., Manikandan, M.S.: A novel method for automatic modulation classification under non-Gaussian noise based on variational mode decomposition. In: 2016 Twenty Second National Conference on Communication (NCC), Guwahati, pp. 1–6 (2016)

    Google Scholar 

  6. Hu, Y.H., Liu, M.Q., Cao, C.F., et al.: Modulation classification in alpha stable noise. In: 2016 IEEE 13th International Conference on Signal, Chengdu, pp. 1275–1278. IEEE (2016)

    Google Scholar 

  7. Amuru, S.D., da Silva, C.R.C.M.: A blind preprocessor for modulation classification applications in frequency-selective non-Gaussian channels. IEEE Trans. Commun. 63(1), 156–169 (2015)

    Google Scholar 

  8. Kebiche, D.E., Baghaki, A., Zhu, X., Champagne, B.: UFMC-based wideband spectrum sensing for cognitive radio systems in non-Gaussian noise. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, pp. 1–7 (2017)

    Google Scholar 

  9. Vinod, A.P., Madhukumar, A.S., Krishna, A.K.: Automatic modulation classification for cognitive radios using cumulants based on fractional lower order statistics. In: 2011 URSI General Assembly and Scientific Symposium, pp. 1–4 (2011)

    Google Scholar 

  10. Hu, S., Pei, Y., Liang, P.P., Liang, Y.: Robust modulation classification under uncertain noise condition using recurrent neural network. In: 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, pp. 1–7 (2018)

    Google Scholar 

  11. Zhang, G.Y., Wang, J., Yang, G.S., et al.: Nonlinear processing for correlation detection in symmetric alpha-stable noise. IEEE Signal Process. Lett. 25(1), 120–124 (2018)

    Article  Google Scholar 

  12. Pardo, A., Real, E., Krishnaswamy, V., et al.: Directional kernel density estimation for classification of breast tissue spectra. IEEE Trans. Med. Imaging 36(1), 64–73 (2017)

    Article  Google Scholar 

  13. Meng, X.L., Rubin, D.B.: Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika 80(2), 267–278 (1993)

    Article  MathSciNet  Google Scholar 

  14. Carvajal, R., Rivas, K., Agüero, J.C.: On maximum likelihood estimation of channel impulse response and carrier frequency offset in OFDM systems. In: 2017 IEEE 9th Latin-American Conference on Communications (LATINCOM), Guatemala City, pp. 1–4 (2017)

    Google Scholar 

Download references

Acknowledgments

The authors acknowledge the financial support of the Key Projects of R&D and Achievement Transformation in Qinghai Province (Grant: 2018-NN-151), the National Natural Science Foundation of China (Grant: 62071364 and 61761040).

Author information

Authors and Affiliations

  1. College of Physics and Electronic Information Engineering, Qinghai Nationalities University, Xining, 810007, China

    Lingfei Zhang

  2. School of Computer, Qinghai Normal University, Xining, 810003, China

    Lingfei Zhang, Jun Ma & Chengqiao Liu

  3. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, 710071, China

    Mingqian Liu

Authors
  1. Lingfei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingqian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chengqiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingqian Liu .

Editor information

Editors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, Tianjin, China

    Xiaofei Wang

  2. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

    Victor C. M. Leung

  3. School of Computer Science and Technology, Tianjin University, Tianjin, Tianjin, China

    Keqiu Li

  4. University of Science and Technology, Beijing, China

    Haijun Zhang

  5. Shenzhen University, Shenzhen, China

    Xiping Hu

  6. National University of Defense Technology, Changsha, China

    Qiang Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, L., Liu, M., Ma, J., Liu, C. (2020). Modulation Recognition with Alpha-Stable Noise Over Fading Channels. In: Wang, X., Leung, V.C.M., Li, K., Zhang, H., Hu, X., Liu, Q. (eds) 6GN for Future Wireless Networks. 6GN 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-63941-9_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63941-9_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63940-2

  • Online ISBN: 978-3-030-63941-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation