Performance Evaluation of Spectrum Sensing in Cognitive Radio for Conventional Discrete-time Memoryless MIMO Fading Channel Model

Spectrum sensing is the crucial task of a cognitive radio. Cognitive Radio (CR) have been advanced as a technology for the opportunistic use of underutilized spectrum where secondary users sense the presence of primary users and use the spectrum if it is empty, without affecting their performance. Spectrum sensing in CR is challenged by a number of uncertainties, which degrade the sensing. The discrete-time memory less multiple inputs multiple output (MIMO) fading channel conventional model is implemented to appraise the performance of different spectrum sensing techniques. The signal detection in CR networks under a non parametric multisensory detection scenario is considered for performance comparison under the presence of impulsive noise. The examination focuses on performance evaluation of five different spectrum sensing mechanisms namely energy detection (ED), Generalized Likelihood Ratio Test (GLRT), Roy’s largest Root Test (RLRT), Maximum Eigenvalue detection (MED) and Cyclostationary feature detection (CSFD). The analysis of the result indicates that, the sensing performance is improved in GLRT method for conventional model also it can be concluded that the performance under the conventional model can be too pessimistic in absence of impulsive noise.