CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Adaptive Spectrum Sensing in Cognitive Radio Networks

by Rishbiya Abdul Gafoor, Riya Kuriakose, Sibila M., Lakshmi C. K., Reshmi S.
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 45
Year of Publication: 2018
Authors: Rishbiya Abdul Gafoor, Riya Kuriakose, Sibila M., Lakshmi C. K., Reshmi S.
10.5120/ijca2018917117

Rishbiya Abdul Gafoor, Riya Kuriakose, Sibila M., Lakshmi C. K., Reshmi S. . Adaptive Spectrum Sensing in Cognitive Radio Networks. International Journal of Computer Applications. 179, 45 ( May 2018), 10-16. DOI=10.5120/ijca2018917117

@article{ 10.5120/ijca2018917117,
author = { Rishbiya Abdul Gafoor, Riya Kuriakose, Sibila M., Lakshmi C. K., Reshmi S. },
title = { Adaptive Spectrum Sensing in Cognitive Radio Networks },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 179 },
number = { 45 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 10-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number45/29434-2018917117/ },
doi = { 10.5120/ijca2018917117 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:58:25.484514+05:30
%A Rishbiya Abdul Gafoor
%A Riya Kuriakose
%A Sibila M.
%A Lakshmi C. K.
%A Reshmi S.
%T Adaptive Spectrum Sensing in Cognitive Radio Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 45
%P 10-16
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The available radio spectrum is not used efficiently, therefore a new technology called Cognitive Radio (CR) is used to increase the spectrum utilization. The objective of CR is to use the available spectrum efficiently without any interference to the Primary Users (PUs). Spectrum sensing plays an essential part in cognitive radio networks inorder to obtain spectrum awareness. Energy detection, matched filter detection, cyclostationary detection etc are the most commonly used techniques for spectrum sensing.This paper proposes an Adaptive spectrum sensing technique in which a particular sensing method from matched filter detection, Energy detection or Wavelet based detection is chosen according to the information available and SNR of the received signal. This paper also investigates the performance of both Eigen value and Wavelet based sensing in low SNR regions.

References
  1. J. Wallace, K. Richardson, B. Gill, and S. Makonin, “Cognitive radio technology: system evolution,” in IEEE, 4th Edition of Int. Conf. on Wireless Networks and Embedded Systems, 2015.
  2. E. FCC, “Docket no 03-222 notice of proposed rule making and order,” 2003.
  3. Ian F. Akyildiz, Won-Yeol Lee, Mehmet C Vuran, Shantidev Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks : A Survey”, Computer Networks vol. 50,no. 13, pp. 2127-2159, 2006.
  4. A .A El Saleh, M Ismail, M Akbari, M R Manesh, and S A R T Zavareh, “Minimizing the detection error of cognitive radio networks using particle swarm optimization”, in Computer and Communication Engineering (ICCCE), 2012 International Conference on, IEEE 2012, pp :877-881.
  5. A Goldsmith, S A Jafar, I Mric, S Srinivasa, “Breaking spactrum gridlock with cognitive radios: An information theoretic perspective”, Proceedings of IEEE, vol 97, no. 5, pp. 894-914, 2009.
  6. G P Joshi, S Y Nam, S W Kim, “Cognitive radio wireless sensor networks : Applications, challenges and research trends”, Sensors 2013, no. 13, pp. 11196-11228
  7. S.Lavanya, B.Sindhuja, M.A.Bhagyaveni, “Implementation of an Adaptive Spectrum Sensing Technique In Cognitive Radio Networks,” International Conference on Computing and Communications Technologies (ICCCT’15)
  8. S.V.R.K.Rao and G.Singh, “ Wavelet Based Spectrum Sensing Techniques in Cognitive Radio”,International Conference on Modeling Optimization and Computing –(ICMOC-2012)
  9. A Ghasemi and E S Sousa, “ Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs”, IEEE Communications Magazine, vol. 46,No. 4, pp 32-39, April 2008.
  10. T Yucek and H Arslan, “A survey os spectrum sensing algorithms for cognitive radio applications”, IEEE Communications Surveys and Tutorials, vol. 11, No. 1, pp. 116-130, March 2009.
  11. Aldo Buccardo, “A Signal Detector for Cognitive Radio System”, University of Gavle, june 2010.
  12. L Bixio, M Ottonello, M Raffetto and C Regazzani, “Comparison among cognitive radio architecture for spectrum sensing”, EURASIP Journal on Wireless communication and network, Genove, Italy, vol. 2011, No. 749891, pp. 1-18, February 2011.
  13. H Urkowitz, “Energy detectionof unknown deterministic signals”, IEEE Proceedings, vol. 55, pp. 523-531, April 1967
  14. H Tang, “Some physical layer issues of wide band cognitive radio systems”, in Proceedings IEEE Int. Symposium on New Frontiers in Dynamic Spectrum Access Networks, USA, pp. 151-159, 2005
  15. Y Zeng and Y C Liang, “Eigen value based spectrum sensing algorithms for cognitive radio”, IEEE Transactions on communications, vol. 57, no. 6, 2009.
  16. Y. Zhao, Y. Wu, J. Wang, X. Zhong, and L. Mei, “Wavelet transform for spectrum sensing in cognitive radio networks,” in Audio, Language and Image Processing (ICALIP), 2014 International Conference on. IEEE, 2014, pp. 565–569.
  17. S Chu, C Burrus, “Multirate filter designs using comb filters”, IEEE Transactions on Circuits and systems, vol. 31,no. 11, pp. 913-924, 1984
Index Terms

Computer Science
Information Sciences

Keywords

Cognitive Radio Spectrum sensing Energy detection Eigen value Wavelet Matched filter

Powered by PhDFocusTM