## 1 Introduction  
With the deep integration of the emerging information technology and wireless communication represented by mobile internet, the internet of things, and cloud computing, radio technology is widely used under the broadband, ubiquitous, and mobile application scenarios, which leads to the explosive growth of spectrum demand, resulting in an extreme shortage of spectrum resources [1]. How to improve the spectrum utilization has become a hot issue and has attracted much attention as a consequence. Cognitive radio (CR), despite being universal, is one of the enabling technologies for the current internet of things (IoT) [2; 3; 4] because it is devoted to searching idle spectrum of the registered user and providing opportunistic access at any time if the radio spectrum of the registered user is unoccupied [5; 6]. As a result, CR has made great contributions to improving spectrum utilization and has developed into an emerging technology. Heterogeneous cognitive radio network (HCRN) [7] denotes a type of network that runs on different protocols to support different functions or applications, due to the differences such as network device, the system, etc. As a result, HCRN is widely discussed for CR because of its universality. The most important procedure behind CR is to periodically detect the observed spectrum and determine the spectrum usage status, named spectrum sensing (SS) [8; 9]. In SS, the registered user is defined as the primary user (PU) and the user waiting for opportunistic access is denoted as the secondary user (SU) [10]. In a SS cycle, if the spectrum of PU is idle, SU will quickly access this band. Once the PU is back, SU will immediately release the corresponding spectrum resources. Generally, the performance of SS is evaluated by two metrics, detection probability and false alarm probability [11]. A higher detection probability corresponds to a stronger capacity of protecting PU from SU while a lower false alarm probability corresponds to a higher system throughput of SU. The CR system with a lower false alarm probability tendsto have a higher throughput [12]. As a result, the combination of detection probability and false alarm probability always works as the performance evaluation index of SS [13]. In the literature, much work has been conducted for SS. Classical SS schemes mainly contain matched filtering [14], energy detection (ED) [15], cyclic spectrum detection [16], covariance matrix detection [17], cooperative spectrum sensing (CSS) [18; 19; 20; 21; 22; 23], etc. When the prior information of the primary signal is known, the matching filtering is the optimal SS scheme. ED shows good characteristics in practical application, but its performance is limited by the time variation of noise power. Cyclic spectrum detection can effectively eliminate noise uncertainty and improve the performance of SS, but it requires a higher sampling rate and a longer spectrum sensing time. Covariance matrix detection is a broadband SS technology, which can effectively improve the efficiency of SS and improve the throughput of the CR system. However, its detection complexity is high and it is impossible to distinguish the number of users when multiple PUs coexist. In the process of SS, the single-node detection is easily affected by channel fading, shadow effect, noise uncertainty, and other factors, resulting in a serious decline in detection performance. However, the influence may be decreased by the centralized data fusion or decision fusion for the CSS. To solve these issues, CSS [18; 19; 20; 21; 22; 23] is proposed, which can significantly improve the sensitivity and the accuracy of SS by data fusion. CSS contains distributed CSS and centralized CSS based on the method of data fusion. In the distributed CSS, the sensing nodes communicate with each other and the sensing decision is made at each node based on the received decision from the other nodes. The centralized CSS makes the decision at the fusion center (FC) based on the sensing conditions of the other nodes.