The objective of this thesis is to explore cognitive radio performance through an in-depth literature review and an implementation of a software-defined radio prototyping system. Specifically, this thesis investigates the spectrum-sensing aspect of cognitive radio by comparing two spectrum-sensing methods. It was found in the literature review that a system utilizing matched filter detection would provide higher probability of detection in low signal-to-noise ratio environments when compared to a system utilizing energy detection. These spectrum sensing methods were thus implemented and compared in the cognitive radio systems presented in this thesis. Additionally, experiments were conducted to determine the most efficient intervals for the spectrum sensing and cycle interval periods. Therefore, system performance was measured on the basis of probability of successful primary user signal detection and maximum throughput capabilities, quantified by bit error rate. It was found that a cognitive radio system based on matched filter detection was more robust, given that the transmitted signal of interest was previously known. However, compared to a system based on energy detection, the implementation of the matched filter required more complex algorithms and computational power. These results are consistent with the findings in the literature review
