Computationally Efficient Compressive Sensing in Wideband Cognitive Radios

Radio spectrum is an expensive resource and only licensed users have the right to use it. In the emerging paradigm of interoperable radio networks, the unlicensed users are allowed to use the radio frequency that is unoccupied by the licensed users in temporal and spatial manner. To support this spectrum optimization functionality, the unlicensed users are required to sense the radio environment periodically for being aware of the high-priority licensed users. Wideband spectrum sensing is a challenging task for the present analog-to-digital converters used in wireless systems due to the constraints of digital signal processing unit. Exploiting on the sparseness of the wideband signal, the spectrum can be recovered with only few compressive measurements, consequently employs relief of high-speed signal processing units. This paper presents a novel wideband sensing approach where a significant portion of wideband spectrum is approximated via compressive sensing rather than entire wideband spectrum estimation, thus reducing computational complexity for the cognitive radios. Detection performances are evaluated through spectrum estimation of the desired frequency band by means of a well-known energy detection method. Finally, reduction of computational burden and memory spaces obligation are described compared to the conventional compressive sensing preceded over a single RF chain, without interfering with the detection performances.