Skip to main content
SpringerLink
Log in

Spectrum Occupancy Classification Using SVM-Radial Basis Function

  • Conference paper
  • First Online:
Cognitive Radio Oriented Wireless Networks (CrownCom 2017)

Included in the following conference series:

Abstract

With recent development in wireless communication, efficient spectrum utilization is major area of concern. Spectrum measurement studies conducted by wireless communication researchers reveals that the utilization of spectrum is relatively low. In this context, we analyzed big spectrum data for actual spectrum occupancy in spectrum band using different machine learning techniques. Both supervised [Naive Bayes classifier (NBC), K-NN, Decision Tree (DT), Support Vector Machine with Radial Basis Function (SVM-RBF)] and unsupervised algorithms [Neural Network] are applied to find the best classification algorithm for spectrum data. Obtained results shows that combination of SVM-RBF is the best classifier for spectrum database with highest classification accuracy appropriately for distinguishing the class vector in the busy and idle state. We made analysis-based on empirical SVM-RBF model to identify actual duty cycle on the particular band across four mid-size location at Ahmedabad Gujarat.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Ding, G., Wu, Q., Wang, J., Yao, Y.-D.: Big spectrum data: the new resource for cognitive wireless networking. arXiv preprint arXiv:1404.6508 (2014)

  2. Sasirekha, G., Dasari, S.R.: Big spectrum data analysis in DSA enabled LTE-A networks: a system architecture. In: 2016 IEEE 6th International Conference on Advanced Computing (IACC), pp. 655–660. IEEE (2016)

    Google Scholar 

  3. MacDonald, J.T.: A survey of spectrum utilization in Chicago. Illinois Institute of Technology, Technical report (2007)

    Google Scholar 

  4. Yucek, T., Arslan, H.: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. 11(1), 116–130 (2009)

    Article  Google Scholar 

  5. Patil, K., Prasad, R., Skouby, K.: A survey of worldwide spectrum occupancy measurement campaigns for cognitive radio. In: 2011 International Conference on Devices and Communications (ICDeCom), pp. 1–5. IEEE (2011)

    Google Scholar 

  6. Islam, M.H., Koh, C.L., Oh, S.W., Qing, X., Lai, Y.Y., Wang, C., Liang, Y.-C., Toh, B.E., Chin, F., Tan, G.L., et al.: Spectrum survey in Singapore: occupancy measurements and analyses. In: 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom 2008, pp. 1–7. IEEE (2008)

    Google Scholar 

  7. Chiang, R.I., Rowe, G.B., Sowerby, K.W.: A quantitative analysis of spectral occupancy measurements for cognitive radio. In: IEEE 65th Vehicular Technology Conference-VTC2007-Spring, pp. 3016–3020. IEEE (2007)

    Google Scholar 

  8. Tugba Erpek, K.S., Jones, D.: Spectrum occupancy measurements, shared spectrum company reports, shared spectrum company, January 2004–August 2005. http://www.sharedspectrum.com/wp-content/uploads/Ireland_Spectrum_Occupancy_Measurements_v2.pdf

  9. Wellens, M., Wu, J., Mahonen, P.: Evaluation of spectrum occupancy in indoor and outdoor scenario in the context of cognitive radio. In: Cognitive Radio Oriented Wireless Networks and Communications, pp. 420–427. IEEE (2007)

    Google Scholar 

  10. Petrin, A., Steffes, P.G.: Analysis and comparison of spectrum measurements performed in urban and rural areas to determine the total amount of spectrum usage. In: Proceedings of the International Symposium on Advanced Radio Technologies (ISART 2005), pp. 9–12 (2005)

    Google Scholar 

  11. Azmat, F., Chen, Y., Stocks, N.: Analysis of spectrum occupancy using machine learning algorithms. IEEE Trans. Veh. Technol. 65(9), 6853–6860 (2016)

    Article  Google Scholar 

  12. Explorer, R.: Handheld hardware. http://www.wimo.com/rf-explorer-spectrum-analyser-signal-generator_e.html

  13. Matheson, R.J.: Strategies for spectrum usage measurements. In: IEEE 1988 International Symposium on Electromagnetic Compatibility, Symposium Record, pp. 235–241. IEEE (1988)

    Google Scholar 

  14. Chen, Y., Oh, H.-S.: A survey of measurement-based spectrum occupancy modeling for cognitive radios. IEEE Commun. Surv. Tutor. 18(1), 848–859 (2014)

    Article  Google Scholar 

  15. López-Benítez, M., Casadevall, F.: Statistical prediction of spectrum occupancy perception in dynamic spectrum access networks. In: 2011 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2011)

    Google Scholar 

  16. Pagadarai, S., Wyglinski, A.M.: Measuring and modeling spectrum occupancy: a massachusetts perspective. In: Proceedings of the International Symposium on Advanced Radio Technologies (2010)

    Google Scholar 

  17. López-Benítez, M., Casadevall, F.: Methodological aspects of spectrum occupancy evaluation in the context of cognitive radio. Eur. Trans. Telecommun. 21(8), 680–693 (2010)

    Article  Google Scholar 

  18. López-Benítez, M., Casadevall, F.: Discrete-time spectrum occupancy model based on Markov chain and duty cycle models. In: 2011 IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), pp. 90–99. IEEE (2011)

    Google Scholar 

  19. Pagadarai, S., Wyglinski, A.: A linear mixed-effects model of wireless spectrum occupancy. EURASIP J. Wirel. Commun. Netw. 2010(1), 1 (2010)

    Article  Google Scholar 

  20. López-Benítez, M., Casadevall, F.: Spatial duty cycle model for cognitive radio. In: 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1631–1636. IEEE (2010)

    Google Scholar 

  21. López-Benítez, M., Casadevall, F.: Spectrum occupancy in realistic scenarios and duty cycle model for cognitive radio. Adv. Electron. Telecommun. Special Issue on Radio Commun. Ser. Recent Adv. Future Trends Wirel. Commun. 1(1), 1–9 (2010)

    Google Scholar 

  22. Ibe, O.: Markov processes for stochastic modeling. Elsevier, Boston (2013)

    MATH  Google Scholar 

  23. López-Benítez, M., Casadevall, F.: Spectrum usage models for the analysis, design and simulation of cognitive radio networks. In: Venkataraman, H., Muntean, G.M. (eds.) Cognitive Radio and its Application for Next Generation Cellular and Wireless Networks. LNEE, vol. 116, pp. 27–73. Springer, Heidelberg (2012). https://doi.org/10.1007/978-94-007-1827-2_2

    Chapter  Google Scholar 

Download references

Acknowledgment

We thank anonymous reviewers and our team members for the continuative support. This work was supported by Gujarat Council on Science and Technology, Department of Science & Technology, Government of Gujarat under the grant GUJCOST/MRP/2015-16/2659. The authors also thank Ahmedabad University for Infrastructure support.

Author information

Authors and Affiliations

  1. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Mitul Panchal, D. K. Patel & Sanjay Chaudhary

Authors
  1. Mitul Panchal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. K. Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjay Chaudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mitul Panchal .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Paulo Marques

  2. Instituto de Telecomunicações, Lisbon, Portugal

    Ayman Radwan

  3. Instituto de Telecomunicações, Lisbon, Portugal

    Shahid Mumtaz

  4. CEA-LETI, Grenoble, France

    Dominique Noguet

  5. Instituto de Telecomunicações, Lisbon, Portugal

    Jonathan Rodriguez

  6. NOKIA, Munich, Germany

    Michael Gundlach

Rights and permissions

Reprints and permissions

Copyright information

© 2018 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

Panchal, M., Patel, D.K., Chaudhary, S. (2018). Spectrum Occupancy Classification Using SVM-Radial Basis Function. In: Marques, P., Radwan, A., Mumtaz, S., Noguet, D., Rodriguez, J., Gundlach, M. (eds) Cognitive Radio Oriented Wireless Networks. CrownCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 228. Springer, Cham. https://doi.org/10.1007/978-3-319-76207-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76207-4_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76206-7

  • Online ISBN: 978-3-319-76207-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation