Performance analysis of Entropy based spectrum sensing in Rayleigh fading for Cognitive radio networks

Abstract

Cognitive Radio Networks presents an opportunity to utilize the vacant spectrum without interfering the licensed primary users (PU). It supports dynamic spectrum access to drive next generation wireless newlinecommunications. Spectrum sensing plays a vital role in cognitive radio which enables secondary users (SU) to detect the presence or absence of licensed PUs. There are many spectrum sensing methods like newlineEnergy detection, Covariance detection, Cyclostationary detection, and Matched filter detection in the literature. The performance of detection methods often relies on radio channel constraints such as multipath fading and noise uncertainty. These conditions increase the missed newlinedetection rate in the low-Signal to Noise (SNR) regime. This work addresses the issue of improving the detection newlineperformance using non-parametric entropy detection techniques in frequency domain under Rayleigh fading channel. In this work, Parzen window entropy based spectrum sensing is proposed for enhancing the SNR wall of PU detection. The single node sensing is extended to cooperative sensing using different weighted gain combining (WGC) fusion schemes. The cooperative node weights of WGC method are computed using log-likelihood ratio (LLR) test and differential evolution (DE) algorithm. The detection performance of the Parzen window entropy technique is compared with Shannon entropy and Energy detection techniques.