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Academic Editor:Rajgopal Kannan
School of Electronic Science and Engineering, National University of Defense Technology, No. 137 Yanwachi Street, Changsha, Hunan 410073, China
Received 9 July 2013; Accepted 4 November 2013
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 Shannon-Nyquist sampling theorem. However, a single CR may fail to detect hidden terminals or LUs due to shadowing or deep fading. In order to alleviate this problem, cooperative spectrum sensing (CSS) [3, 4] that exploits the built-in spatial diversity among multiple CRs has been proposed for CR networks. In addition, cooperation is especially useful for CS-based approaches since compressed reconstruction is quite susceptible to noise and the performance degrades severely when the signal to noise ratio (SNR) is low. Based on how cooperative CRs share their sensing data in the network, CSS can be classified into two categories: centralized CSS and distributed CSS. In centralized CSS scheme, a fusion center (FC) is required to collect measurements from all CRs and make centralized sensing decisions. Centralized CSS schemes using CS theory are presented in [11, 12]. The performance achieves global optimization; however, the incurred power cost and communication load in transmitting...