Research On Spectrum Sensing In Cognitive Radio Systems

With the rapid development of wireless communication technology, the limited spectrum resource has failed to meet the growing demand and it has become a bottleneck restricting the development of wireless communications. Cognitive radio technology is proposed as a solution to this problem. It can improve the utilization of the spectrum effectively and ease the current tensions of situation. Spectrum sensing is the basis in cognitive radio applications. In this paper, we mainly discuss and research it.Single-user spectrum sensing algorithms are introduced firstly, and the advantages and disadvantages of these algorithms are compared. Due to multipath fading and shadow fading, the test results of single cognitive user are not accurate, so in Chapter III cooperative spectrum sensing is proposed to improve the accuracy. In cooperative spectrum sensing, information fusion is the key to its implementation, different fusion algorithms have different effects on system performance; this chapter mainly discusses the decision fusion and the data fusion, and then compares the performance of these algorithms.Compared to the method of fixed sample size, the chapter IV mainly researches on non-fixed number of samples using sequential testing methods. It has the advantages of requiring a small number of samples and saving channel resource; the lack of it is that when it has many malicious nodes, the detection performance is poor. In order to improve the detection probability and achieve the decision threshold with fewer samples while there are malicious users, a modified weighted sequential probability ratio test based on the nodes ability of correct decision is proposed. In this method, a better weighted coefficient based on ability of correct decision is given, and the malicious users can be found and the data transmitted by them can be utilized. Simulation results show that, with fewer samples, the proposed method obtains a higher detection probability than the original algorithm.At last, the article combines compressed sensing technology and spectrum sensing together, studies the wideband spectrum sensing based on compressed sensing technology. In the compressed sensing technology, it is important how to reconstruct the original signal from the compressed samples. In the reconstruction algorithm, we use regularized orthogonal matching pursuit algorithm with a better stability. When it is applied to spectrum sensing, because part of the spectrum has always been in use, we can consider the part of spectrum as the known information. An modified regularized orthogonal matching pursuit algorithm (GOMP) is proposed in this paper. Simulation results demonstrate that the improving algorithm reconstructs the data more accurately under the same conditions.