The need for RF spectrum for the rapidly growing broadband access services is evident. Cognitive radio is an emerging technology that aims to introduce secondary usage of the spectrum resources without interfering with the primary usage of the licensed users but with a lower priority.

   Signal detection for cognitive radios has drawn a lot of interest in the research community, where different algorithms are suggested. The most commonly used algorithms are energy detection, feature detection, eigenvalue based detection. Energy detection is the simplest and most common way to detect signals. It has fast sensing time but poor performance. The feature detection and eigenvalue based detection methods are more sophisticated and offer better performance but they are more complex and expensive. This thesis will present the pros and cons of each method and offer comparisons between them.

   To evaluate the performance of different algorithms used in cognitive radio, different research testbeds have been suggested in the literature. Some of the most frequently used testbeds are based on GNU-radio, WARP, or BEE2. GNU-radio is the simplest testbed and is free, but it has low bandwidth and poor performance. WARP and BEE2 are more advanced testbeds. They offer good performance and are easy to update, but they are more complex and expensive. These three testbeds will be described, compared, and their advantages and disadvantages will be observed in this thesis.