Distributed Compressive Wideband Spectrum Sensing in Cooperative Multi-Hop Cognitive Networks

This paper develops a distributed compressed spectrum sensing approach for cooperative wideband multi-hop cognitive radio (CR) networks where both primary users and CR users are active during the sensing stage. Due to the multi-hop nature, each CR needs to sense its individual spectral map that consists of both common spectral components from primary users and individualized spectral innovations arising from emissions of other CRs or interference in its local one-hop region. These CR-dependent spectral innovation components complicate the task of user cooperation for primary user detection. To cope with these difficulties, we adopt the compressed sensing approach at local CRs to attain high-resolution signal recovery at lower-than-Nyquist sampling rates. Each CR alternatively estimates the spectral occupancy of primary and CR users, and exchanges proper information with neighboring CRs to reach global fusion and consensus on the estimated primary user spectrum. The spectral orthogonality between primary users and CR users is exploited to improve the spectral estimation accuracy. Using only one-hop local communications, the proposed distributed algorithm converges fast to the globally optimal solution at low communication and computation load scalable to the network size.