Research On Collaborative Spectrum Sensing Technology In Cognitive Network

Cognitive radio technology plays an important role in the development of wireless communication technology. With the rapid development of wireless communication technology and continual increase of wireless applications demand, the available radio spectrum resources has gradually diminished. In order to solve the problem of scarcity of spectrum resources and improve the utilization of spectrum resources, spectrum sensing technology as a prerequisite and prerequisite of cognitive radio technology has become a hotspot at home and abroad.In the real environment, the signal transmission is often affected by the fading and shadowing hence single node spectrum sensing technology could not meet the reliability requirements. So cooperative spectrum sensing technology as a reliable model has become an important development direction of cognitive radio spectrum sensing technology. In this paper, we focus on the cooperative spectrum sensing technology, and mainly discuss the clustering cooperation awareness and fusion criterion in the centralized cooperative spectrum sensing system.The main innovations and work of this paper are as follows1. In this paper, a local detection scheme based on position correlation function and dual threshold energy detection is proposed. In the real wireless communication environment, the sensing nodes are often affected by shadow and fading. Single node detection could not satisfy the reliability requirement. Therefore in this paper, the cooperative spectrum sensing of multi-sensing nodes is adopted. However, the perceptual information of each sensing node reaching the fusion center through the report channel that increased the communication bandwidth and system loss reduces the performance of the entire system. To solve this problem,we introduce the clustering strategy and use the position correlation function to divide the perceived users into multiple clusters. So in this paper, the double-threshold energy detection is proposed. When the perceptual information is bigger than the threshold 1, the primary user exists. When the perceptual information is little than the threshold 2, the primary user does not exist. The perception of information between the threshold 1 and threshold 2 does not participate the decision in the cluster head. The simulation results shows that the clustering algorithm reduces the system loss compared with the non-clustering algorithm. The energy detection based on double threshold improves the reliability of the system compared with the single threshold detection scheme. And the detection probability of the whole system has been improved.2. This paper proposes a fusion scheme of 1-bit local decision based on trust function. The local detection results of the cluster head are transmitted to the fusion center through the report channel, and the data are merged according to the fusion rules. At last, the final decision result has been obtained. The result of the 1-bit decision reduces the transmission bandwidth, improves the transmission rate and minimizes delay effectively.Compared with the hard-decision scheme, the soft-decision strategy increases the system overhead, but the soft-decision has different coefficients for the different perceptual nodes. The soft-decision strategy could improve the detection rate and system reliability. Simulation results shows that the 1-bit data transmission scheme reduces the channel bandwidth effectively. The detection probability based on the soft-decision strategy has been improved compared with the hard-decision strategy.3. Finally, the paper summarizes the work, and the next step is predicted.