Evaluation of relative performance of different detection methods for spectrum sensing in cognitive radio networks under impulsive noise

In this paper, the methodology for 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). The signal detection in CR networks under a non-parametric multisensory detection scenario is considered for performance comparison under the presence of unknown or inaccurately known impulsive noise levels. 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 estimate a noise variance. The closed form analysis of the results under the presence of impulsive noise show that GLRT method performs better than the other techniques in realistic implementation oriented model as compared to the conventional discrete time memory less MIMO fading channel model.