Power spectrum estimation and PAPR analysis for Cognitive Radio Networks

Abstract

Most of the existing works consider the estimation of power spectrum. However, they did not provide the implementation of power spectrum estimation. In order to provide an efficient solution, in this paper, we propose power spectrum estimation and PAPR analysis for Cognitive Radio Networks (CRN). In this technique, power spectrum value of each node is calculated by computing autocorrelation. This power spectrum value is compared with five methods namely Periodogram spectral estimate, Bartlett's spectral estimate, Welch spectral estimate, Blackman Tukey spectral estimate and Correlogram spectral estimate. The difference in transmitted signal can be measured in terms of Peak-to-Average-Power-Ratio (PAPR). Finally, PAPR analysis is performed using the complementary cumulative distribution function (CCDF). The proposed technique is simulated in MATLAB.