OPTIMIZATION OF ENERGY DETECTION APPROACH OF SPECTRUM SENSING IN COGNITIVE RADIO NETWORK

Abstract

Cognitive Radio is the emerging technology that allows dynamic access of radio spectrum. Spectrum Sensing is the first task needed to be done to check the presence of licensed users in the spectrum. This thesis is focused on understanding the underlying principles of “Energy detection for Spectrum Sensing” in Cognitive Radio technology which does not need any prior information about the type of signal and optimizing its performance. In this research, spectrum sensing algorithms basically Energy Detection (ED) is considered under a typical fading unknown channel and White Gaussian Noise scenario. Knowledge of the noise power is imperative for the optimum performance of ED. Unfortunately the variation and unpredictability of noise power is unavoidable. Introducing an idea of auxiliary noise variance estimation for combating the absence of prior knowledge of noise power, Hybrid Energy Detection 1 (HED1)/Hybrid-2 (HED2) approach of signal detection was set forth. For HED noise variance is estimated in S auxiliary noise only slots and for HED2 noise variance is estimated in S auxiliary slots which are declared only noise signal slots by ED. The detection performance of the considered methods are derived and expressed by a closed form analytical formulas. The impact of noise estimation accuracy on the performance of ED is compared based on Receiver Operating Characteristic curves and Performance Curves. Accordingly, this study shows that even if the performance gap may be significant under some circumstances (few sensors, low signal-to-noise ratio, small number of slots used for noise power estimation), the performance gap can be decreased in terms of ROC performance by increasing the number of slots used for noise variance estimation.