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

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Distributed Compressive Wideband Spectrum Sensing in Cooperative Multi-hop Cognitive Networks
Fanzi Zeng
School of Computer and Communications Hunan University Changsha, Hunan, P. R. China Email: zengfanzi@
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2010 proceedings
Zhi Tian
Department of Electrical and Computer Engineering Michigan Technological University Houghton, MI 49931 USA Email: ztian@
Chen Li
Department of Electrical and Computer Engineering Michigan Technological University Houghton, MI 49931 USA Email: cheli@
I. I NTRODUCTION For spectrum sensing in (ultra-)wideband cognitive radio (CR) networks, the major challenges are the high sampling rates and the wireless fading. For the sampling rate issue, recent advances in compressive sampling (CS) have indicated that sparse signals can be reliably recovered at sub-Nyquist sampling rate via computationally feasible algorithms [1]. Based on the CS principle, compressed spectrum sensing schemes have been presented for wideband CR sensing in a single point-to-point link [2], [3]. To cope with the undesired effects of wireless fading on spectrum sensing, collaborative fusion can be performed among multiple spatially distributed CRs in the network. Several distributed compressive sampling schemes have been proposed for user collaboration, all of which rely on a fusion center [4]–[6]. For network robustness, it is preferred to take a decentralized approach for robust information processing without the use of a fusion center. Along this line, [7] has
presented a distributed compressed sensing framework for wideband communication networks. As in most literature on cooperative spectrum sensing, [7] assumes that all CRs stay silent such that only the primary users (PUs) are emitting spectrum power during the sห้องสมุดไป่ตู้ectrum detection. This practice facilitates user cooperation, but renders low throughput and inefficient spectrum utilization of the CR network. This paper investigates multi-hop CR networks where both primary users and CR users can be active during the sensing stage. As shown in Fig. 1, primary users typically transmit at high power and hence can be heard by all CRs, giving rise to the common sparse spectrum support perceived by each CR’s sensing unit. Meanwhile, low-power CRs constitute a multi-hop CR network, where each CR can hear from some but not all the other CRs. 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. The underlying signal model as such is discussed by the joint sparsity model in [8]. These CR-dependent spectral innovation components can only be perceived by individual CRs and hence have to be identified at local CRs, which complicate the task of network-wide user cooperation for primary user detection. To deal 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 users’ spectra. The spectral orthogonality between primary and CR users is exploited to improve the spectral estimation accuracy. Using only one-hop local communication, the proposed distributed algorithm converges fast to the globally optimal solution at low communication and computation load scalable to the network size. Corroborating simulation results are presented to show the effectiveness of the proposed compressed sensing and distributed fusion techniques in identifying spectral hole opportunities for multi-hop CR networks.
Abstract—1 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 CRdependent 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 lowerthan-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. Index Terms—cognitive radio, compressed spectrum sensing, distributed fusion, collaborative sensing, consensus.