Call For Paper Volume:4 Issue:8 Aug'2017 |

Comparative performance evaluation of Spectrum Sensing Techniques for Cognitive Radio in Next Generation Wireless Networks

Publication Date : 28/02/2015

Author(s) :

Ms.Pratiksha D.Nandanwar , Dr.M.M.Khanapurkar.

Volume/Issue :
Volume 2
Issue 2
(02 - 2015)

Abstract :

In recent years there has been an enormous growth in wireless communication devices and wireless users. The availability of the spectrum is most important for fulfillment of the demand. Spectrum is a valuable resource in communication. So to fulfill the demand we either need more spectrums or make efficient use of current available spectrum. But as spectrum resources are limited we need to use them efficiently. It is impossible to use spectrum efficiently with the static spectrum allocation policy. Due to this static policy most of the spectrum remains underutilized. To use spectrum efficiently we need to use dynamic spectrum allocation policy. For future wireless communication cognitive radio is the key in expertise. Spectrum sensing is one of the most important functions in cognitive radio (CR) applications. Cognitive radio technology is used as the problem solution key in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. Sensing of spectrum availability has been identified as a key requirement for dynamic spectrum allocation in cognitive radio networks. it involves the detection of primary user (PU) transmissions on a pre assigned frequency band. Primary user licensed band can be sensed via suitable spectrum sensing methodologies. This paper presents three basic spectrum sensing techniques of transmitter detection: energy detection, matched filter detection, and cyclostationary feature detection. A proportional scrutiny of all the techniques has been carried out in terms of probability of detection alarm Pd, probability of false alarm Pf and probability of missed detection pm using simulations. As a final point, result shows that at low signal to noise ratio (SNR), cyclostationary feature detection outperforms better than the rest techniques. For simulation matlab software is used.So in this paper authors are presenting the comparative performance analysis of these three techniques.

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Comparative performance evaluation of Spectrum Sensing Techniques for Cognitive Radio in Next Generation Wireless Networks

February 24, 2015