The research of spectrum compressive sensing using wireless microphone signals

Spectrum sensing is a fundamental component in Cognitive Radio networks. Some detection methods for spectrum sensing in IEEE 802.22 Wireless Regional Area Networks (WRAN) were proposed. In this paper, a sensing algorithm of wireless microphone signals based on compressive sensing, Fast Fourier Sampling (FFS), is analyzed and compared with the traditional spectrum sensing algorithms. Although the detection performance of FFS algorithm is lower than that of other detectors, FFS complies with IEEE 802.22 constrains of time at far less processing complexity and lower sample rate. Furthermore, the simulation shows that FFS can be used to process long size sparse signals with same time approximately.