Table of Contents
International Scholarly Research Notices
Volume 2014 (2014), Article ID 579125, 11 pages
http://dx.doi.org/10.1155/2014/579125
Research Article

A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application

1Department of Telecommunication, Federal University of Technology, Minna, Niger State, Nigeria
2Digital Bridge Institute, Abuja, Nigeria
3Department of Mechatronic Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia

Received 31 March 2014; Revised 9 June 2014; Accepted 1 July 2014; Published 29 October 2014

Academic Editor: George Kyriacou

Copyright © 2014 A. J. Onumanyi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. López-Benítez and F. Casadevall, “On the spectrum occupancy perception of cognitive radio terminals in realistic scenarios,” in Proceedings of the 2nd International Workshop on Cognitive Information Processing (CIP '10), Special Session on NEWCOMTF, pp. 99–104, Elba, Italy, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. A. J. Onumanyi, E. N. Onwuka, O. Ugweje, and M. J. E. Salami, “Spectroscopic measurements and analysis of frequency occupancy: a case study of Minna, Niger State,” in Proceedings of the 3rd Biennial Engineering Conference, pp. 30–35, Federal University of Technology, Minna, Nigeria, 2013.
  3. R. Urban, T. Kriz, and M. Cap, “Indoor broadband spectrum survey measurements for the improvement of wireless systems,” in Proceedings of the Progress in Electromagnetics Research Symposium (PIERS '13), pp. 376–380, Taipei, Taiwan, March 2013. View at Scopus
  4. M. N. Mehdawi, K. Riley, A. Paulson, M. Fanan, and M. Ammar, “Spectrum occupancy survey in HULL-UK for cognitive radio applications: measurement & analysis,” International Journal of Scientific & Technology Research, vol. 2, no. 4, pp. 231–236, 2013. View at Google Scholar
  5. D. Datla, A. M. Wyglinski, and G. J. Minden, “A spectrum surveying framework for dynamic spectrum access networks,” IEEE Transactions on Vehicular Technology, vol. 58, no. 8, pp. 4158–4168, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Polson and B. A. Fette, “Cognitive techniques: position awareness,” Cognitive Radio Technology, pp. 265–288, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Wang and K. J. R. Liu, “Advances in cognitive radio networks: a survey,” IEEE Journal on Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5–23, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Hur, J. Park, W. Woo, J. S. Lee, K. Lim, and C. Lee, “A cognitive radio (CR) system employing a dual-stage spectrum sensing technique: A Multi-Resolution Spectrum Sensing (MRSS) and a Temporal Signature Detection (TSD) technique,” in Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '06), pp. 1–5, San Francisco, Calif, USA, December 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Nekovee, “Mechanism design for cognitive radio networks,” in Proceedings of the Conference on Complexity in Engineering (COMPENG '10), pp. 12–17, Rome, Italy, February 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. E. Hossain, D. Niyato, and D. I. Kim, “Evolution and future trends of research in cognitive radio: a contemporary survey,” Wireless Communications and Mobile Computing, 2013. View at Publisher · View at Google Scholar
  11. I. F. Akyildiz, W. Lee, M. C. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey,” Computer Networks, vol. 50, no. 13, pp. 2127–2159, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Umar and A. U. H. Sheikh, “A comparative study of spectrum awareness techniques for cognitive radio oriented wireless networks,” Physical Communication, vol. 9, pp. 148–170, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Dhope and D. Simunic, “Spectrum sensing algorithm for cognitive radio networks for dynamic spectrum access for IEEE 802.11af standard,” International Journal of Research and Reviews in Wireless Sensor Networks, vol. 2, no. 1, pp. 77–84, 2012. View at Google Scholar
  14. D. Cabric, S. M. Mishra, and R. W. Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” in Proceedings of the Conference Record of the 38th Asilomar Conference on Signals, Systems and Computers, pp. 772–776, Pacific Grove, Calif, USA, November 2004. View at Scopus
  15. A. Ghasemi and E. S. Sousa, “Optimization of spectrum sensing for opportunistic spectrum access in cognitive radio networks,” in Proceedings of the 4th Annual IEEE Consumer Communications and Networking Conference, pp. 1022–1026, January 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. D. G. Altman and J. M. Bland, “Parametric v non-parametric methods for data analysis,” BMJ, vol. 338, Article ID a3167, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Kay, “Detection of a Sinuisoid in White Noise by Autoregressive Spectrum Analysis,” Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '80), pp. 658–661, 1980. View at Google Scholar · View at Scopus
  18. P. Stoica and L. M. Randolph, Spectral Analysis of Signals, Pearson/Prentice Hall, Upper Saddle River, NJ, USA, 2005.
  19. A. Gorcin, H. Celebi, K. A. Qaraqe, and H. Arslan, “An autoregressive approach for spectrum occupancy modeling and prediction based on synchronous measurements,” in Proceedings of the IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '11), pp. 705–709, Toronto, Canada, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. Z. Wen, T. Luo, W. Xiang, S. Majhi, and Y. Ma, “Autoregressive spectrum hole prediction model for cognitive radio systems,” in Proceedings of the IEEE International Conference on Communications Workshops (ICC '08), pp. 154–157, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Dong, Y. Dong, and L. Wang, “Autoregressive channel prediction model for cognitive radio,” in Proceedings of the 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM '09), pp. 1–4, Beijing, China, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Dong, Y. Dong, L. Wang, Z. Yang, and H. Zhang, “Particle filtering based autoregressive channel prediction model,” Journal of Electronics, vol. 27, no. 3, pp. 316–320, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. A. M. Aibinu, M. J. E. Salami, and A. A. Shafie, “Artificial neural network based autoregressive modeling technique with application in voice activity detection,” Engineering Applications of Artificial Intelligence, vol. 25, no. 6, pp. 1265–1276, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. P. Welch, “The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms,” IEEE Transactions on Audio and Electroacoustics, vol. 15, no. 2, pp. 70–73, 1967. View at Publisher · View at Google Scholar
  25. M. Matinmikko, H. Sarvanko, M. Mustonen, and A. Mämmelä, “Performance of spectrum sensing using welch's periodogram in rayleigh fading channel,” in Proceedings of the 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 5, p. 1, Hannover, Germany, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. I. Harjula, A. Hekkala, M. Matinmikko, and M. Mustonen, “Performance evaluation of spectrum sensing using welch periodogram for OFDM signals,” in Proceedings of the IEEE 73rd Vehicular Technology Conference (VTC '11), pp. 1–5, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. N. Wang and G. Yue, “Optimal threshold of Welch's periodogram for sensing OFDM signals at low SNR levels,” in Proceedings of the 19th European Wireless Conference (EW), pp. 1–5, Guildford, UK, April 2013.
  28. S. Haykin, “The multitaper method for accurate spectrum sensing in cognitive radio environments,” in Proceeding of the 41st Asilomar Conference on Signals, Systems and Computers of the IEEE Conference Record (ACSSC '07), pp. 436–439, Pacific Grove, Calif, USA, November 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Wang and Q. T. Zhang, “A multitaper spectrum based detector for cognitive radio,” in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '09), pp. 1–5, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. H. Gao, M. Wu, C. Xu, and Q. Wu, “An improved multitaper method for spectrum sensing in cognitive radio networks,” in Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT '10), pp. 393–396, Chengdu, China, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. H. Urkowitz, “Energy detection of unknown deterministic signals,” Proceedings of the IEEE, vol. 55, no. 4, pp. 523–531, 1967. View at Publisher · View at Google Scholar