Detection Proposal Schemes for Spectrum Sensing in Cognitive Radio
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Abstract
The most important components of the cognitive radio concept is its ability to measure, sense and learn. One also should be aware of the parameters related to the radio channel characteristics and the availability of spectrum and power. In cognitive radio technology, primary users can be defined as the users who have the highest priority on the usage of a specific part of the spectrum. Secondary users, have lower priority, and should not cause any interference to the primary users when using the technology. Therefore, the secondary users need to have certain cognitive radio capabilities, such as sensing the spectrum to check whether it is being used by primary user or not, and changing the radio parameters to exploit the unused part of the spectrum. In this paper we proposed a new approach for spectrum sensing, In the first approach the primary signal is known so we use the code value with match filter to detect the primary user, on the other hand, when the primary user signal is unknown we proposed a new strategy for energy detection in both non-cooperation and cooperation schemes. Then we will prove by simulation results that the new approach is better than the conventional energy detection.
- L. F. Akyildiz, W. Y. Lee, M. C. Vuran and S. Mohanty “Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey,” Broadband and Wireless Networking Laboratory, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 2006.
- H. Tang, “Some Physical Layer Issues of Wide-Band Cognitive Radio Systems,” Proceedings of IEEE Int. Symposium on the New Frontiers in Dynamic Spectrum Access Networks, Baltimore, November 2005, pp. 151-159.
- C. X. Wang, H. H. Chen, X. Hong and M. Guizani, “Cognitive Radio Network Management,” IEEE Vehicular Technology Magazine, Vol. 3, No. 1, 2008, pp. 28-35.
- M. Barkat, “Signal Detection and Estimation,” Artech House, 1991.
- H. L. V. Trees, “Detection, Estimation and Modulation Theory,” Part1, John Wiley and Sons, Inc., 1968.
- H. V. Poor, “An Introduction to Signal Detection and Estimation,” Springer-Verlag, Second Edition, 1994.
- A. Papoulis, “Probability, Random Variable, and Stochastic Processes,” McGrow-Hill, 1985.
- D. Cabric, S. M. Mishra and R. W. Brodersen, “Implementation Issues in Spectrum Sensing for Cognitive Radios,” Proceedings of Asilomar Conference on Signals, Systems and Computers, Pacific Grove, Vol. 1, November 2004, pp. 772-776.
- S. Shankar, C. Cordeiro and K. Challapali, “Spectrum Agile Radios: Utilization and Sensing Architectures,” Proceedings of IEEE Int. Symposium on New Frontiers in the Dynamic Spectrum Access Networks, Baltimore, November 2005, pp.160-169.
- H. Urkowitz, “Energy Detection of Unknown Deterministic Signals,” Proceedings of IEEE, Vol. 55, April 1967, pp. 523-531.
- P. D. Welch, “The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging over Short, Modified Periodograms,” Transaction on the Audio and Electroacoustics, Vol. 23, June 1967, pp. 70-73.
- I. S. Gradshteyn and I. M. Ryzhik, “Table of Integral, Series and Products,” Fifth Edition, Academic Press, 1994.
- A. H. Nuttall, “Some Integrals Involving QM Function” IEEE Transaction on Information Theory, Vol. 21, No. 1, January 1975, pp. 95-96.
- F. F. Digham, M. S. Alouini and M. K. Simon, “On Energy Detection of Unknown Signal over Fading Channel” Proceedings of IEEE Internation Conference on Communication (ICC03), May 2003, pp. 3575-3579.