Skip to main content

Spectrum Sensing for Fading Wireless Channel Using Matched Filter

  • Conference paper
  • First Online:
Book cover Soft Computing for Problem Solving

Abstract

Ever-increasing use of wireless applications is exerting pressure on the limited, insufficient, and expensive licensed spectrum. Actually, because of allocation of fixed spectrum, more portion of spectrum is underutilized. Spectrum sensing can be used for the efficient and effective use of the radio spectrum. It detects the unused spectrum channels in cognitive radio network. In cognitive radio, spectrum sensing techniques such as energy detection, matched filter detection have been used. In this paper, receiver operating characteristics (ROC) of matched filter and energy detector are compared at −10 and −15 dB signal-to-noise Ratio (SNR) levels in fading wireless channels. From the results obtained it is found that matched filter system performed well over the ROC of energy detector at lower values of SNR.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)

    Article  Google Scholar 

  2. Kay, S.M.: Fundamentals of Statistical Signal Processing, Estimation Theory, vol. I. Prentice hall (1993)

    Google Scholar 

  3. Kay, S.M.: Fundamentals of Statistical Signal Processing, Detection Theory, vol. I. Prentice hall (1998)

    Google Scholar 

  4. Van Trees, H.L.: Detection, Estimation, and Modulation theory. Wiley (2004)

    Google Scholar 

  5. Ali, A., Hamouda, W.: Spectrum monitoring using energy ratio algorithm for OFDM-based cognitive radio networks. IEEE Trans. Wirel. Commun. 14(4), 2257–2268 (2015)

    Article  Google Scholar 

  6. Xuping, Z., Jianguo, P.: Energy-detection based spectrum sensing for cognitive radio, pp. 944–947 (2007)

    Google Scholar 

  7. Plata, D.M.M., Reátiga, Á.G.A.: Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold. Procedia Eng 35(2012): 135–143

    Article  Google Scholar 

  8. Ling, F.: Matched filter-bound for time-discrete multipath Rayleigh fading channels. IEEE Trans. Commun. 43(234), 710–713 (1995)

    Article  Google Scholar 

  9. Xuping, Z., Jianguo, P.: Energy-detection based spectrum sensing for cognitive radio, pp. 944–947 (2007)

    Google Scholar 

  10. Ling, F.: Matched filter-bound for time-discrete multipath Rayleigh fading channels. IEEE Trans. Commun. 43(234), 710–713 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suresh Dannana .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dannana, S., Chapa, B.P., Rao, G.S. (2019). Spectrum Sensing for Fading Wireless Channel Using Matched Filter. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_27

Download citation

Publish with us

Policies and ethics