Performance Evaluation of Different Spectrum Sensing Techniques for Realistic Implementation Oriented Model in Cognitive Radio Networks

Abstract

In this paper, the performance assessment of five different detection techniques from spectrum sensing perspective in cognitive radio networks is proposed and implemented using the realistic implementation oriented model (R-model) with signal processing operations. The performance assessment of the different sensing techniques in the existence of unknown or imprecisely known impulsive noise levels is done by considering the signal detection in cognitive radio networks under a non-parametric multisensory detection scenario. The examination focuses on performance comparison of basic spectrum sensing mechanisms as, energy detection (ED) and cyclostationary feature detection (CSFD) along with the eigenvalue-based detection methods namely, Maximum-minimum eigenvalue detection (MMED), Roy's largest Root Test (RLRT) which requires knowledge of the noise variance and Generalized Likelihood Ratio Test (GLRT) which can be implemented as a test of the largest eigenvalues vs. Maximum-likelihood estimates a noise variance. From simulation results it is observed that the detection performance of the GLRT method is better than the other techniques in realistic implementation oriented model.