Cyclostationarity-Based Spectrum Sensing for Analog TV and Wireless Microphone Signals

Spectrum sensing is a vital signal processing task in the cognitive radio systems in order to detect and identify the primary users signals (the licensees). In this paper, we study the performance of cyclostationary features detector as a spectrum sensing technique for IEEE 802.22 Wireless Regional Area Network (WRAN). We propose two algorithms based on cyclostationary features to detect the presence of TV-PAL and wireless microphone signals. These algorithms are evaluated in comparison with energy detector. The results show that the performance of cyclostationarity-based algorithms is superior to energy detector performance especially in the case of noise uncertainty.