Skip to main content
SpringerLink
Log in

Deep Learning Based Digital Signal Modulation Recognition

  • Conference paper
The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 322))

Abstract

In this investigation, we proposed a promising digital signal modulation recognition scheme which is inspired by the deep learning. Firstly, the signal discriminations are constructed, which are composed of the full temporal characteristics of digital signals, its frequency spectrum as well as several higher-order spectral characteristics. Subsequently, the deep learning algorithm, with the powerful ability of interpretations and learning, is further suggested to realize modulation recognitions. A major advantage of this new scheme is that it may fully exploit the complete information of digital signals, rather than only utilizing several extracted features. It is verified by experimental simulations that the recognition accuracy of the proposed new scheme is much superior to other traditional recognition methods, which therefore provides an attractive approach to realistic modulation recognition.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover 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. Nandi AK, Azzouz EE (1998) Algorithms for automatic modulation recognition of communication signals. IEEE Trans Cornrn 46(4):431–436

    Article  Google Scholar 

  2. Azzouz EE, Nandi AK (1996) Automatic modulation recognition of communications signals. Kluwer, Boston

    Book  Google Scholar 

  3. Chen H, Liu C (2013) Research and application of cluster analysis algorithm[C]. In: Measurement, information and control (ICMIC), 2013 international conference on IEEE, vol 1, pp 575–579

    Google Scholar 

  4. Hassan K, Dayoub I, Hamouda W et al. (2010) Automatic modulation recognition using wavelet transform and neural networks in wireless systems. EURASIP J Adv Signal Process

    Google Scholar 

  5. Savitha R, Suresh S, Sundararajan N (2012) Metacognitive learning in a fully complex-valued radial basis function neural network. Neural Comput 24(5):1297–1328

    Article  MathSciNet  Google Scholar 

  6. Hinton G, Deng L, Yu D et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. Signal Process Mag IEEE 29(6):82–97

    Article  Google Scholar 

  7. Hinton G (2010) A practical guide to training restricted Boltzmann machines. Momentum 9(1):926

    Google Scholar 

Download references

Acknowledgement

This work was supported by National Science and Technology Major Project (2013ZX03001015-003), NSFC (61379016, 61271180), Doctoral Fund of Ministry of Education of China (20130005110016).

Author information

Authors and Affiliations

  1. Key Lab of Universal Wireless Communications, MOE Wireless Network Lab, Beijing University of Posts and Telecommunications, Beijing, China

    Junqiang Fu, Chenglin Zhao, Bin Li & Xiao Peng

Authors
  1. Junqiang Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenglin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiao Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junqiang Fu .

Editor information

Editors and Affiliations

  1. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, Tianjin, China

    Jiasong Mu

  2. University of Texas at Arlington, Arlington, Texas, USA

    Qilian Liang

  3. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, Tianjin, China

    Wei Wang

  4. Tianjin Normal University College of Physical and Electronic Infor, Tianjin, China

    Baoju Zhang

  5. University of Electronic Science and Tec School of Electronic Engineering, Chengdu, China

    Yiming Pi

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Fu, J., Zhao, C., Li, B., Peng, X. (2015). Deep Learning Based Digital Signal Modulation Recognition. In: Mu, J., Liang, Q., Wang, W., Zhang, B., Pi, Y. (eds) The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-08991-1_100

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08991-1_100

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08990-4

  • Online ISBN: 978-3-319-08991-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation