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

Estimation of an Improved Spectrum Sensing Threshold for Cognitive Radio using Smoothed Pseudo Wigner-Ville Distribution

by W. O. Ajadi, S. M. Sani, A. M. S. Tekanyi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 12
Year of Publication: 2017
Authors: W. O. Ajadi, S. M. Sani, A. M. S. Tekanyi
10.5120/ijca2017914503

W. O. Ajadi, S. M. Sani, A. M. S. Tekanyi . Estimation of an Improved Spectrum Sensing Threshold for Cognitive Radio using Smoothed Pseudo Wigner-Ville Distribution. International Journal of Computer Applications. 168, 12 ( Jun 2017), 30-33. DOI=10.5120/ijca2017914503

@article{ 10.5120/ijca2017914503,
author = { W. O. Ajadi, S. M. Sani, A. M. S. Tekanyi },
title = { Estimation of an Improved Spectrum Sensing Threshold for Cognitive Radio using Smoothed Pseudo Wigner-Ville Distribution },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 12 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 30-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number12/27929-2017914503/ },
doi = { 10.5120/ijca2017914503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:59.341225+05:30
%A W. O. Ajadi
%A S. M. Sani
%A A. M. S. Tekanyi
%T Estimation of an Improved Spectrum Sensing Threshold for Cognitive Radio using Smoothed Pseudo Wigner-Ville Distribution
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 12
%P 30-33
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cognitive radio (CR) has been suggested as the solution to spectrum scarcity due to the fixed allocation employed worldwide by regulatory bodies. In order to avoid interference to a primary user signal, the CR has to be aware about the spectrum usage in the geographic area in which it wants to operate. The process of spectrum sensing is a fundamental task for obtaining this awareness and the result of this process determines the successful operation of cognitive radio. Energy detection is one of the methods of spectrum sensing with the lowest computational complexity but with low performance at low signal to noise ratio. Exploring energy detection has led to the application of many techniques one of which is the use of time-frequency analysis. This method employs distribution techniques for analyzing the energy spectral density of an observed signal with a view to setting a sensing threshold. However, the distribution techniques that were used in literature suffered from the problem of cross-terms which affect the analysis of the resulting distribution thereby leading to poor sensing performance at low signal-to-noise ratio. Smoothed pseudo Wigner-Ville distribution of the time-frequency analysis has been employed in this work to reduce the effect of cross-terms for better sensing threshold. Simulation results evaluate the performance of the employed technique compared to pseudo Wigner-Ville for AWGN, Rician and Rayleigh channel conditions.

References
  1. Biglieri, E., Goldsmith, A. J., Greenstein, L. J., Mandayam, N. B., & Poor, H. V. (2012). Principles of cognitive radio: Cambridge University Press.
  2. McHenry, M., & McCloskey, D. (2004). New York City spectrum occupancy measurements september 2004. Shared Spectrum Company, www. sharedspectrum. com.
  3. Aguilar-Gonzalez, R., Cardenas-Juarez, M., Rico, U. P., & Stevens-Navarro, E. (2013). Power Spectrum Measurements from 30 MHz to 910 MHz in the City of San Luis Potosi, Mexico. Procedia Technology, 7, 30-36.
  4. Babalola, O., Garba, E., Oladimeji, I., Bamiduro, A., Faruk, N., Sowande, O., Muhammad, M. (2015). Spectrum occupancy measurements in the TV and CDMA bands. Paper presented at the 2015 International Conference on Cyberspace (CYBER-Abuja).
  5. Ayeni, A. A., Faruk, N., Bello, O. W., Sowande, O. A., Onidare, S. O., & Muhammad, M. Y. (2016). Spectrum Occupancy Measurements and Analysis in the 2.4-2.7 GHz Band in Urban and Rural Environments. International Journal of Future Computer and Communication, 5(3), 142.
  6. Angrisani, L., Betta, G., Capriglione, D., Cerro, G., Ferrigno, L., & Miele, G. (2014). Proposal and analysis of new algorithms for wideband spectrum sensing in cognitive radio. Paper presented at the 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.
  7. Vaidehi, G., Swetha, N., & Sastry, P. N. (2015). Entropy based Spectrum Sensing in Cognitive Radio Networks. Entropy, 4(11).
  8. Javed, F., & Mahmood, A. (2010). The use of time frequency analysis for spectrum sensing in cognitive radios. Paper presented at the 2010 4th International Conference on Signal Processing and Communication Systems (ICSPCS)
  9. Monfared, S. S., Taherpour, A., & Khattab, T. (2013). Time-frequency compressed spectrum sensing in cognitive radios. Paper presented at the Global Communications Conference (GLOBECOM), 2013 IEEE.
  10. Bektas, C., Akan, A., & Odabasioglu, N. (2012). Energy based spectrum sensing using wavelet transform for fading channels. Paper presented at the 2012 4th International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops (ICUMT)
  11. Javed, F., Shafi, I., & Mahmood, A. (2012). A novel radio mode identification approach for spectrum sensing in cognitive radios. International Journal of Communication Networks and Information Security, 4(2), 86.
  12. Biagi, M., Rinauro, S., Colonnese, S., Scarano, G., & Cusani, R. (2014). WiVCoRA: Wigner–Ville Cognitive Radio Access for Secondary Nodes. Vehicular Technology, IEEE Transactions on, 63(9), 4248-4264.
  13. Hiremath, S., Patra, S., & Mishra, A. (2015). Spectrum Sensing for Cognitive Radio using S-method based Joint Time-Frequency Representation.
  14. Boashash, B. (2015). Time-frequency signal analysis and processing: a comprehensive reference: Academic Press.
  15. Ingram, D. M. A., Acosta, G., & Matlab, O. S. U. (2000). Smart Antenna Research Laboratory. Guillermo Acosta August.
  16. Auger, F., Flandrin, P., Goncalves, P., & Lemoine, O. (2005). Time-Frequency Toolbox for use with MATLAB. Retrieved from http://tftb.nongnu.org/
Index Terms

Computer Science
Information Sciences

Keywords

Cognitive radio Spectrum sensing Time-Frequency Analysis Smoothed Pseudo Wigner-Ville distribution Cross-terms.

Powered by PhDFocusTM