Improved Throughput Performance in Wideband Cognitive Radios via Compressive Sensing

Abstract

Wideband spectrum sensing is a challenging task due to the constraints of digital signal processing (DSP) unit using in extant wireless systems. Compressive sensing (CS) is a new paradigm in signal processing, chosen for sparse wideband spectrum estimation with compressive measurements, thus provides relief of high-speed DSP requirements of cognitive radio (CR) receivers. In CS, whole wideband spectrum is estimated to find an opportunity for a CR usage requiring significant computation as well as sensing time, hence shrinkage the achievable throughput of CRs. In this paper, a novel model based CR receiver wideband sensing unit is addressed where a significant portion of the wideband spectrum is approximated through compressive sensing rather than recovering the total wideband spectrum. This model necessitates lesser sensing time and lower computational burden to detect a signal and as a result a level up of throughput is obtained. As a result, the sensing time gain improves the achievable throughput of the CRs which reflects on the simulation results and testifies the effectiveness of the proposed model. Therefore, a reduction of computational complexity is addressed without interfering with the detection performances, evaluated after spectrum estimation of a preferred band of interest by means of a well-known energy detector.