Enhanced Performance in Wideband Cognitive Radios via Compressive Sensing

Abstract — In the emerging paradigm of interoperable network, the cognitive users are allowed to transmit opportunistically on a temporarily empty frequency band which is authorized to the licensed users. To support this spectrum sharing functionality, the cognitive users dynamically sense the radio frequency environment for being aware of the high-priority licensed users. Spectrum sensing becomes challenging in the wideband regime due to high sampling frequency functioning at or above Nyquist rates. Based on the sparseness of the wideband signal, the spectrum can be recovered with only few compressive measurements, thus employs relief of high-speed signal processing units. This paper proposes an efficient way for wideband cognitive receiver sensing unit that estimate the highly sparse segment of wideband through compressed sensing rather than entire wideband spectrum and then discover spectral opportunity for a cognitive user. The proposed model deals with the highly-sparse signal segment which provides better spectral estimation and hence improves the detection performance, demonstrated by the simulation. Eventually, reduction of computational complexity as well as a level up of detection performance of the proposed method has sorted out compared to a single RF chain followed by compressive sensing. Index terms—compressive sensing, l1-norm minimization, analog-to-information converter, wideband spectrum sensing, spectrum estimation. I