Distributed Compressed Spectrum Sensing via Cooperative Support Fusion

Spectrum sensing in wideband cognitive radio (CR) networks faces several significant practical challenges, such as extremely high sampling rates required for wideband processing, impact of frequency-selective wireless fading and shadowing, and limitation in power and computing resources of single cognitive radio. In this paper, a distributed compressed spectrum sensing scheme is proposed to overcome these challenges. To alleviate the sampling bottleneck, compressed sensing mechanism is used at each CR by utilizing the inherent sparsity of the monitored wideband spectrum. Specifically, partially known support (PKS) of the sparse spectrum is incorporated into local reconstruction procedure, which can further reduce the required sampling rate to achieve a given recovery quality or improve the quality given the same sampling rate. To mitigate the impact of fading and shadowing, multiple CRs exploit spatial diversity by exchanging local support information among them. The fused support information is used to guide local reconstruction at individual CRs. In consideration of limited power per CR, local support information percolates over the network via only one-hop local information exchange. Simulation results testify the effectiveness of the proposed scheme by comparing with several existing schemes in terms of detection performance, communication load, and computational complexity. Moreover, the impact of system parameters is also investigated through simulations. 1. Introduction Spectrum sensing, whose objectives are detecting signal of licensed users (LUs) and identifying the spectrum holes for dynamic spectrum access (DSA) [1], is an important enabling technology for cognitive radio, a leading choice for efficient utilization of spectrum resource [2–4]. In wideband cognitive radio networks, cognitive radio could attain more spectrum access opportunities in wideband regime. On the other hand, the task of wideband spectrum sensing entails several major challenges, such as very high signal acquisition cost in wideband scenario, uncertain channel fading and random shadowing, and limitation in power and computational capability per CR. To alleviate the heavy pressure on the conventional analog to digital converter (ADC) technology, compressed sensing (CS) theory [5–7] has been introduced into the application of wideband spectrum sensing by utilizing the low percentage of spectrum occupancy, a fact that motivates dynamic spectrum access [8–10]. CS theory states that sparse signal can be reconstructed from much fewer samples than suggested by the