Multi-taper detection method (MTM) is a powerful technique in spectrum sensing for Cognitive radio networks. In this paper, reliable and simple analytical expressions for the mean and variance of the Probability Density Function (PDF) of the MTM spectrum detector are derived. Then, closed-form expressions for detection and false alarm probabilities for the MTM spectrum detector have been obtained. Intensive simulation based work is conducted under AWGN channel conditions using MATALB to confirm and evaluate the proposed theoretical study. The confirmation and the evaluation processes are designated to verify many perspectives such as: the receiver operating characteristics (ROCs), the detection rate with respect to SNR, and minimum required sample points to achieve a certain performance. All these perspectives are simulated under setting of multiple Slepian tapers (), sample points and false-alarm probability (). Also, a comparison with energy detection method is presented. The simulation results confirm that the proposed model is reliable and robust under all settings of the simulation parameters.
Selim, H. A., Soliman, H., & Dessouki, A. (2018). Reliable Analytical Approach for Multi-taper Spectrum Sensing in Cognitive Radio Networks. Port-Said Engineering Research Journal, 22(2), 56-63. doi: 10.21608/pserj.2018.32100
MLA
Heba Allah Selim; Heba Soliman; Ahmed Ahmed Shaaban Dessouki. "Reliable Analytical Approach for Multi-taper Spectrum Sensing in Cognitive Radio Networks", Port-Said Engineering Research Journal, 22, 2, 2018, 56-63. doi: 10.21608/pserj.2018.32100
HARVARD
Selim, H. A., Soliman, H., Dessouki, A. (2018). 'Reliable Analytical Approach for Multi-taper Spectrum Sensing in Cognitive Radio Networks', Port-Said Engineering Research Journal, 22(2), pp. 56-63. doi: 10.21608/pserj.2018.32100
VANCOUVER
Selim, H. A., Soliman, H., Dessouki, A. Reliable Analytical Approach for Multi-taper Spectrum Sensing in Cognitive Radio Networks. Port-Said Engineering Research Journal, 2018; 22(2): 56-63. doi: 10.21608/pserj.2018.32100