## 1 Introduction  
With the rapid development of wireless communication technology, the number of wireless users has been increasing dramatically, resulting in the shortage of spectrum resources. However, according to the investigation of the Federal Communication Commission (FCC), low utilization of the registered spectrum is not a new problem [1]. In consequence, how to improve the utilization of the registered spectrum has become a problem that deserves our attention [2]. To solve the shortage issue of the wireless spectrum as much as possible, Cognitive Radio (CR) [3; 4; 5] technology is proposed, where a CR can adaptively adjust its parameters with the change of environment for opportunistic spectrum reuse when the primary (registered) user (PU) is out. The main technology behind CR includes spectrum sensing (SS) [6; 7; 8], dynamic spectrum allocation (DSA) [9] and spectrum sharing [10]. SS mainly contributes to the decision on spectrum occupancy. The main research of DSA focuses on how to effectively optimize and allocate the perceived spectrum. Spectrum sharing aims at exploring high-efficiency sharing of the observed spectrum in the condition of multiple users. Since it functions as the foundation of DSA and spectrum sensing, SS appears indispensable for further development of CR. Classical SS methods mainly contain energy detector (ED) [11], cyclostationary feature detector (CFD) [12], covariance matrix-based detector (CMD) [13] and cooperative spectrum sensing (CSS) [14]. Thanks to its low complexity and superior performance, ED is the most popular and classical scheme for SS [15]. However, ED is sensitive to environment noise and its sensing performance degrades sharply with the fall of environmental SNR. Consequently, most ED-based SS are devoted to restraining noise uncertainty. CFD has attracted the attention of many researchers due to its insensitivity to noise uncertainty [16], which is at the expense of computational complexity. In consequence, CFD works as a kind of SS scheme suitable for the situation, where there is no constraint on system complexity. CMD is based on the covariance matrix analysis of the received observed signal [17]. The essence of CMD is that received signals are correlated with each other; however, there is no correlation between noise and noise, as well as between noise and signals. The main purpose of CSS is to solve the hidden terminal issue where high buildings may interfere with the received signal of CR and results in judgment on the condition of observed spectrum bands [18]. By placing various CR terminals located in different positions, CSS helps to solve the hidden terminal issue and improves sensing accuracy as well. CSS contains centralized CSS and distributed CSS [19]. Distributed CSS communicates with each other and each node makes the final decision based on the decision of the others [20]. For centralized CSS, there is a fusion center where the decision result or received signal of each node is sent to the fusion center and the fusion center makes the final decision according to some decision rule [21]. The classical decision rules of centralized CSS contain AND rule [22], OR rule [23], Majoring voting rule [24], Equal Gain Combining rule [25], Selection Combining rule [26] and Maximal Ratio Combining rule [27]. In SS, three parameters are associated with the sensing performance, detection probability, false-alarm probability and available throughput [28]. The higher the detection probability is, the better the primary user (PU) can be protected. From the perspective of secondary users, the lower the false-alarm probability is, the greater chance the channel can be reused when it is available. Nevertheless, when detection probability is high, the corresponding false-alarm probability is also high, resulting in a lower available throughput. On the contrary, when the false alarm probability is at a low level, the corresponding detection probability is also comparatively low.