Spectrum Sensing Framework based on Blind Source Separation for Cognitive Radio Environments
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Dec 14, 2015
Published:
Nov 7, 2015
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Abstract
The efficient use of spectrum has become an active research area, due to its scarcity and underutilization. In a spectrum sharing scenario as Cognitive Radio (CR), the vacancy of licensed frequency bands could be detected by a secondary user through spectrum sensing techniques. Usually, this sensing approaches are performed with a priori knowledge of the channel features. In the present work, a blind spectrum sensing approach based on Independent Component Analysis and Singular Spectrum Analysis is proposed. The approach is tested and compared with other outcomes. Results show that the proposed scheme is capable of detect most of the sources with low time consumption, which is a remarkable aspect for online applications with demanding time issues.
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Sepúlveda Cano, L. M., Quiza Montealegre, J. J., Gil Taborda, C., & Gómez García, J. A. (2015). Spectrum Sensing Framework based on Blind Source Separation for Cognitive Radio Environments. Revista Ingenierías Universidad De Medellín, 15(29), 129–140. https://doi.org/10.22395/rium.v15n29a8
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References
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4. G. Nautiyal & R. Kumar, 'Spectrum sensing in cognitive radio using matlab', International Journal of Engineering and Advanced Technology (IJEAT), vol. 2, no. 5, pp. 529-532, Jun. 2013.
5. Z. Xuping & P. Jianguo, 'Energy-detection based spectrum sensing for cognitive radio', in iet Conference on Wireless, Mobile and Sensor Networks (ccwmsn07), Shangai, China, Dec. 12-14, 2007, pp. 944-947.
6. H. Arslan, Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems (Signals and Communication Technology). New York: Springer-Verlag, 2007.
7. M. Rahman, A. Haniz, S. Khadka, S., Iswandi, Gahadza, M., Kim, M., ichi Takada, J. 'Development of spectrum sensing system with gnu radio and usrp to detect emergency radios', ieice, The Institute of Electronics, Information and Communication Engineers, Sendai, Japan, Technical Report SR2009-57, Oct. 2009.
8. A. Fehske, J. Gaeddert & J. Reed, 'A new approach to signal classification using spectral correlation and neural networks', in DySPAN 2005. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, USA, Nov. 8-11, 2005. pp. 144-150.
9. S. Chaudhari, 'Spectrum sensing for cognitive radios: Algorithms, performance, and limitations', Ph. D. thesis, Aalto University, Greater Helsinki, Finland, 2012.
10. S. Da, G. Xiaoying, C. Hsiao-hwa & Q. Liang, 'Fast cycle frequency domain feature detection for cognitive radio systems', Arxiv, p. 4, Ar. 6, 2009. Available: https://archive.org/details/arxiv-0903.1183
11. M. Calabro, 'A Cooperative Spectrum Sensing Network with Signal Classification Capabilities'. Ph. D. thesis, Worcester Polytechnic Institute, Worcester, Massachusetts, 2010.
12. A. G. Ferrer, E.G. Prieto & D. Peña, 'Exploring ica for time series decomposition', Working Paper 11-16, Statistics and Econometrics Series 11, May 2011. Available: http://orff.uc3m.es/bitstream/handle/10016/11285/ws111611.pdf?sequence=1
13. L. Molgedey & H. G. Schuster, 'Separation of a mixture of independent signals using time delayed correlations', Physical Review Letters, vol. 72, 3634-3637, 1994.
14. V. Krishnaveni, S. Jayaraman, P. M. Kumar, K. Shivakumar & K. Ramadoss, 'Comparison of independent component analysis algorithms for removal of ocular artifacts from electroencephalogram', Meas. Sci. Rev. J, vol. 5, no. 2, pp. 67-78, 2005.
15. Hongli, Sun, Y.: 'The study and test of ica algorithms', in 2005 Proceedings International Conference on Wireless Communications, Networking and Mobile Computing, vol. 1, Wuhan, China, Sept. 23-26, 2005, pp. 602-605.
16. T. Kolenda, L. K. Hansen & J. Larsen, 'Signal detection using ica: Application to chat room topic spotting', in 3rd International Conference on Independent Component Analysis and Blind Source Separation, ica’2001, San Diego, USA, Dec. 9-13, 2001, pp. 540-545. Available: http://cogsys.imm.dtu.dk/publications/2001/kolenda.ica2001.pdf
17. H. G. Ma, Q. B. Jiang, Z. Q. Liu, G. Liu & Z. Y. Ma, 'A novel blind source separation method for single-channel signal', Signal Processing, vol. 90, no. 12, pp. 3232-3241, 2010.
18. S. S. Kalamkar & A. Banerjee, 'On the performance of generalized energy detector under noise uncertainty in cognitive radio', in National Conference on Communications (ncc), Delhi, India, Feb. 15-17, 2013. pp. 1–5.