Energy Detection in Cognitive Radio Network (CRN) under Rayleigh Nakagami-m Fading Channels

Abstract

The performance of a Cognitive radio network depends on, how successfully the Secondary User (SU) can detect the present of Primary User (PU) and recognize the false alarm arises from interferences. In this research project, we determine the profile of probability of detection and probability of false alarm of a Secondary User under Rayleigh and Nakagami-m fading channels. Here, we use the combination of fading and Additive White Gaussian Noise (AWGN) as the impairments of the signal. We detect both the above parameters analytically and verified by Monte Carlo Simulation. The analytical and simulation results are found very closed to each other.