Research Article

Channel Optimization By Using Spectrum Sensing Techniques In Cognitive Radio Network

Volume: 12 Number: 2 June 30, 2020
Rustem Yilmazel *, Nihat İnanç
EN

Channel Optimization By Using Spectrum Sensing Techniques In Cognitive Radio Network

Abstract

In recent years, the rapid development of wireless technologies has increased the need for frequency spectrum usage. To overcome this problem, Cognitive Radio (CR) has emerged as a new technique which enables the use of free ranges in the frequency spectrum. Cognitive radio networks (CRNs) are based on the principle of benefiting from the empty spectrum of secondary users (Sus) by using sensing techniques and analyzing frequency ranges. Although the frequency spectrum is used by various technologies, some frequency ranges are used inefficiently and inadequately. Fixed spectrum allocations cause unused frequency channels in the radio frequency spectrum. Spectrum detection, which is one of the features that cognitive radio applies to reduce the inefficiency of spectrum usage, scans all the primary users (PUs) in the spectrum area and the empty frequency bands. In this study, a method has been proposed to detect unused stationary frequency bands and to use these frequency bands effectively. This method, which is named as matched filter detection , was examined in AWGN channel and its results were evaluated in MATLAB.

Keywords

Matched filter detection , cognitive radio , spectrum sensing

References

  1. Ahmed, E., Yao, L. J., Shiraz, M., Gani, A. and Ali S. (2013) ‘Fuzzy based Spectrum Handoff and Channel Selection for Cognitive Radio Networks’, Conference Proceedings, IEEE International Conference on Computer, Control, Informatics
  2. Bragança, H., Diogo, E., Moniz, F., Amaro, P. (2009). First report of pitch canker on pines caused by Fusarium circinatum in Portugal. Plant Disease, 93(10), 1079-1079.
  3. Chinh Chu, T.M., Phan, H., Zepernick, H.J., “Dynamic Spectrum Access for Cognitive Radio Networks with Prioritized Traffics”, IEEE Communications Letters, 18, 1218-1221, 2014.
  4. Coutinho, P.S., Rocha Da Silva, M.W., De Rezende, J.F., “Detection Error Aware Spectrum Handoff Mechanism for Cognitive Radios”, International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications, 48-53. Stockholm, Sweden, 2012.
  5. Fahmi, M., Ghasemi, A., “Analysis of the PRP M/G/1 Queuing System for Cognitive Radio Networks with Handoff Management”, Iranian Conference on Electrical Engineering, 1047-1051. Tehran, Iran, 2014.
  6. Federal Communications Commission (FCC), “Spectrum policy task force,” ET Docket No. 02-135, November 2002.
  7. Han, H., Wu, Q. and Yin, H. (2010) ‘Spectrum Sensing for Real Time Spectrum Handoff in CRNs’, Conference Proceedings, IEEE International Conference on Advanced Computer Theory and Engineering, Chengdu, pp. V1-480 – V1-484.
  8. Kamra, S.K. (1989). Improving the forest seed situation in some African countries. Turnbull, J. W. (Ed.) Tropical Tree Seed Research. Gympie, Australia.
  9. Khattab A. (2013) Cognitive Radio Networks: From Theory to Practice, New York: Springer Science & Business Media.
  10. Konishi, Y., Masuyama, H., Kasahara, S. and Takahashi, Y. (2013) ‘Performance Analysis of Dynamic Spectrum Handoff Scheme with Variable Bandwidth Demand of Secondary Users for Cognitive Radio Networks’, Wireless Networks Journal, vol. 19, no. 5, July, pp. 607- 617.
APA
Yilmazel, R., & İnanç, N. (2020). Channel Optimization By Using Spectrum Sensing Techniques In Cognitive Radio Network. International Journal of Engineering Research and Development, 12(2), 693-699. https://doi.org/10.29137/umagd.726756