Efficient blind spectrum sensing for cognitive radio networks based on compressed sensing

Abstract

Spectrum sensing is a key technique in cognitive radio networks (CRNs), which enables cognitive radio nodes to detect the unused spectrum holes for dynamic spectrum access. In practice, only a small part of spectrum is occupied by the primary users. Too high sampling rate can cause immense computational costs and sensing problem. Based on sparse representation of signals in the frequency domain, it is possible to exploit compressed sensing to transfer the sampling burden to the digital signal processor. In this article, an effective spectrum sensing approach is proposed for CRNs, which enables cognitive radio nodes to sense the blind spectrum at a sub-Nyquist rate. Perfect reconstruction from fewer samples is achieved by a blind signal reconstruction algorithm which exploits ℓp-norm (0 < p < 1) minimization instead of ℓ1 or ℓ1/ℓ2 mixed minimization that are commonly used in existing signal recovery schemes. Simulation results demonstrated that the ℓp-norm spectrum reconstruction scheme can be used to break through the bandwidth barrier of existing sampling schemes in CRNs.