## 1 Introduction  
Cognitive radio (CR) has attracted considerable attention for its ability to maximize the rate of usage of limited spectrum resources [1; 2; 3]. The essence of the CR concept is opportunistic spectrum sharing [4]. Specifically speaking, this technology allows non-licensed users (secondary users, SUs) to opportunistically use primary users' (PUs) frequency band resources for data transmission while the PUs are idle. These spectrum resources that are used by SUs during a specific period of time are referred to as'spectrum holes'. So, the performance of CR depends highly on the successful detection of the corresponding spectrum holes. In a traditional sensing process in CR, time is divided into fixed portions which are called time slots. Each time slot is divided into a small period of spectrum sensing (silence period) and a relatively long period of data transmission [5]. In the sensing period, SUs use various techniques to sense if the PUs are active or idle. This is the typical \"listen-before-talk\" paradigm [6]. In the literature, several types of spectrum sensing schemes have been proposed. When the PU's signal waveform and channel noise properties are known a priori, the matched filter detector is optimal [7]. The cyclostationary feature detection method requires a priori knowledge about the cyclic frequencies of the PU signals [8]. The energy detection method is usually simple to implement since it does not require knowledge about the PU signal. However, it does require the knowledge of the involved noise variance. The above three common spectrum sensing technologies are classified into the first type of spectrum sensing algorithm because they need to know the signal and/or channel information. Because these three typical spectrum sensing techniques need to know signal and/or channel information, they are grouped into the same group. The second group of detectors for spectrum sensing are based on eigenvalue properties of an estimated correlation matrix. Because the eigenvalue-based spectrum sensing techniques may operate without knowledge of the PU signal [8] and noise variance [4], they can be used in a totally blind manner. The current research results in this area include the maximum eigenvalue detector [9], the maximum-minimum eigenvalue detector [9], the decentralized eigenvalue algorithms [10], the sequential likelihood ratio test scheme [11], and so on. These algorithms may offer a remarkably improved performance for specific signal categories. However, this is done at the expense of simplicity. Different complexity spectrum detection algorithms will bring different degrees of delay. This will affect the proportion of the silence period in a time slot. Because no data transmission takes place during the silence period, an excessive silence period will reduce the SU's throughput. So the management algorithm of the silence period is one of the key issues to improve the efficiency of spectrum sensing and time utilization. In order to reduce the spectrum sensing time, an optimization algorithm which combines energy detection and feature detection is proposed [12]. Since energy detection focuses only on the energy level of the channel, it requires only a relatively short period of time for spectrum sensing. This method, through the results of energy detection, can determine whether it is needed to start feature detection or not, so it can improve the time utilization rate on the basis of ensuring sufficient detection accuracy in the silent periods. In [13], a mechanism to reduce the length of the silent period is given. When the number of continuous energy detection alarms is higher than a certain preset value, the characteristic detection is triggered. The Markov analysis is used to study the decision threshold of the energy detection results. In [14], a method of spectrum sensing without the silence period is proposed in which the secondary user (SU) performs spectrum sensing for the primary users (PU) while carrying out the data transmission.