Spectrum congestion has become a critical concern in wireless communication systems due to the limited availability of frequency spectrum. Hence, efficient utilization of spectrum is one of the most important challenges in the evolution of wireless communi-cation systems and radio devices. Cognitive radio (CR) has been introduced as an effec-tive solution for spectrum utilization. Spectrum sensing (SS) is one of the key elements in the implementation of effective and reliable CR systems. SS algorithms are used to obtain awareness about the spectrum usage and existence of primary users in a certain spectrum band. Energy detection (ED) based SS is the most common sensing algorithm due to its low computation and implementation complexity. On the other hand, ED based SS is highly dependent on the precise knowledge of the receiver noise variance. Hence, the performance of the ED algorithm is degraded significantly, when there is uncertainty in the estimation of the noise variance.
In this thesis, the wireless microphone (WM) system using the CR concept is intro-duced and the sensing performance of WM signals using three different algorithms are studied. The considered algorithms are based on the ED, namely fast Fourier transform (FFT) based ED, analysis filter bank (AFB) based ED and maximum-minimum ED (Max-Min ED) are studied. Following the analytical models and scenarios of energy detector based SS algorithms, the sensing algorithms are implemented using National Instruments’ (NI) Universal Software Radio Peripheral (USRP) and the NI-LabVIEW software platform, together with the necessary toolboxes. This prototype implementa-tion provides reliable performance evaluation of these spectrum sensing approaches us-ing real world receiver implementation and communication signals from a signal genera-tor, as well as actual WM signals. The results of this study suggest that the performance of Max-Min ED is more robust than FFT & AFB based ED under realistic noise vari-ance uncertainty
