# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 1 Introduction  
The rapid development of wireless communication technology has led to more and more wireless network services. The radio spectrum, as the most valuable resource in wireless networks, cannot meet the requirements of wireless services at present and in the future [1]. The existing fixed spectrum allocation method makes the spectrum utilization low and seriously uneven. According to the investigation, the average spectrum utilization is less than 5% at any time or place [2]. Dynamic spectrum access (DSA) is considered to be the main technical solution to the contradiction between supply and demand [3]. As the basis of DSA, cognitive radio (CR) technology has become one of the most cutting-edge research topics in the field of wireless networking. CR has the ability to interact with its communication environment and change its transmission parameters according to the result. The cognitive radio network (CRN) [4, 5, 6] is a network with CR as the service terminal. In the CRN, the licensed user and the unlicensed user coexist, where the licensed user is named the primary user (PU) and the unlicensed user is called the secondary user (SU). The CR users with CR can sense idle spectrum resources and maximize the utilization of spectrum resources without interfering with PU. The research work of the CRN is mainly focused on spectrum sensing (SS), spectrum sharing, spectrum access and spectrum mobility [7]. Different from the channel allocation of traditional wireless networks, the channel allocation of the CRN is based on the real-time sensing of channel conditions [8]. As a result, SS is the foundation of CR. The main function of SS is to detect the spectrum holes available to SU and to monitor the signal activity of the PU to ensure that when the PU uses the spectrum again [9], SU can quickly exit the corresponding frequency band. Two factors influence the performance of SS, namely false alarm probability and detection probability. The false alarm probability is negatively correlated with the throughput of the CR, while the detection probability denotes the protection capacity for PU. Classical SS schemes contain single node-basedSS and cooperative spectrum sensing (CSS). Single node-based SS contains matched filter detection [10], energy detection (ED) [11], cyclostationary feature detection [12] and covariance matrix detection [13], etc. However, the single node-based SS either has poor performance or has high complexity [7]. In the case of the hidden terminal issue (the shadow and the deep fading), the sensing results of the single node-based SS are not reliable, so it is necessary to integrate the sensing results of multiple nodes in order to improve the reliability of detection, namely CSS. Classical CSS schemes contain centralized CSS and distributed CSS [14; 15; 16; 17; 18]. Centralized CSS denotes that each node aggregates the local sensing information to the fusion center, and the fusion center makes a decision according to the fusion rules. Finally, the fusion center (FC) feeds back the decision result to each local node [15; 16]. According to the fusion rule at the FC, centralized CSS contains the AND rule-based CSS, the OR rule-based CSS and the majority voting (MV)-based CSS, etc. There is no fusion center for distributed CSS [17; 18], and each local node exchanges the detection results with the others, and then combines the local fusion decision. The sensing accuracy of the CR system is effectively improved for the distributed CSS [19; 20; 21]. However, this is at the expense of network burden and system overhead [22; 23; 24]. In consequence, centralized CSS is mainly considered in this paper. With the rapid development of artificial intelligence (AI) technology, deep learning has attracted much attention due to its capacity of automatic feature extraction [25; 26; 27]. As one of the most successful technologies of AI, the convolution neural network (CNN) has been successfully applied to the field of wireless communication [28; 29].