Cognitive Radio Spectrum Sensing Technology And Its Performance Research

With the rapid development of information technology, data communication and cloud computing business is growing explosively. Vast spectrum is occupied by a large number of communication services, it makes available spectrum resources nervously and hindered the development of wireless communication technology. Cognitive radio technology can active learning through to the surrounding environment, to judge the situation of utilization of spectrum, and the user can access using the authorized frequency band opportunity, so as to improve spectrum utilization.In this paper, the spectrum sensing for cognitive radio technology is studied, he basic knowledge of cognitive radio technology and research methods is introduced firstly, and then summarizes the common cooperative spectrum sensing algorithm, and has made the research from two different aspects of spectrum sensing.To improve the detection performance and reliability, we assume a cooperative scheme by improved energy detector. The algorithm gives different weight to different CR by surroundings of CRs, in order to weaken the disadvantage of bad CRs. The simulation results show that the new algorithm can significantly improve the performance of spectrum sensing. Then, we discuss the double threshold energy detection based on variable weight. Aiming at the shortcomings of traditional double threshold energy sensing algorithm, we propose the new double threshold energy detection algorithm based on traditional method. It will dynamically update the weight of each cognitive user according to detection probability of each cognitive user through maximum ratio combination weights. We can find that this algorithm has obvious effect in improving the detection probability and reduce the probability of false alarm.According to the channel efficiency and energy efficiency, energy detection and cooperative spectrum sensing is used as the basic method. The channel efficiency and the energy efficiency is measuring firstly, and then, energy efficiency are studied, so we get comprehensive analysis of the various indicators of cognitive radio network. Both the theoretical analysis and simulating results show that the user can select the best sensing duration and the proper threshold, that optimize the channel efficiency and energy efficiency.