WIRELESS MICROPHONE SIGNAL DETECTION USING A COMPRESSIVE SENSING TECHNIQUE for IEEE 802.22

The speed and accuracy of spectrum sensing techniques are essential factors in the performance of cognitive radio networks. The limitations imposed by computational complexity and a shortened monitoring time impede the success of spectrum sensing operation performed by cognitive radio nodes. Compressive sensing techniques are viewed as novel approaches to solve scalability problems in some signal processing operations. One popular application of compressed sensing is sizable image recovery. This technique can be used in spectrum sensing applications to reduce the barriers of current spectrum sensing computational requirements. The successful application of these techniques will result in faster sensing operations, less complex sensing modules, or wider spectrum sensing capabilities. The coming IEEE 802.22 air interface standard aims to provide wireless services in regional area networks using TV spectrum white spaces. This standard is considered the first standard that is based on a cognitive radio approach. Spectrum sensing is a critical functionality that needs to be performed by 802.22 compliant devices. While, the standard does not specify any spectrum sensing method, it requires the sensing operation to be performed within timing and accuracy constraints. This simulation study investigates the impact of different configuration parameters of a compressive sensing technique called Fast Fourier Sampling to detect wireless microphone signals as dictated by the IEEE 802.22 air interface.