Features detection assisted spectrum sensing in wireless regional area network cognitive radio systems

Spectrum sensing is a key enabling technique for implementing cognitive radio (CR) networks. Based on the detection of primary users' signals, a CR can fully exploit wireless radio resources, thus to increase spectrum efficiency and allow opportunistic transmissions for secondary users. This work presents a spectrum sensing approach for its applications in a wireless regional area network (WRAN) based on features detection of advanced television systems committee (ATSC) digital TV and WRAN signals over a Rayleigh fading channel. The scheme aims at detecting and identifying both ATSC and WRAN signals. To improve spectrum sensing performance in low-signal-to-noise ratio (SNR) regions, the characteristics of both ATSC and WRAN signals are exploited in spectrum sensing algorithms based on a correlation-based feature identification approach. In this study, real working scenarios of a WRAN CR network are considered. The effectiveness of the proposed detector has been verified by simulations.