Research On Algorithm Of Cooperative Spectrum Sensing In Cognitive Radio

Cognitive Radio (CR) technology has great significance in the history of the development of wireless communication. It allows CR users making use of the idle spectrum resources, improves the spectral efficiency, and solves the dilemma that the spectrum resources get scarce to a certain extent which at the same time has also played a significant role in promoting the development of the intelligent network, the reform of the spectrum allocation and the application of the adaptive transmission strategy. Spectrum sensing technology, as the basis for cognitive radio system, has great value and significance on the function of the whole CR system.After a long period of research and development, the CR technology and the spectrum sensing technology still have some challenges, despite we've made rich results in these fields. In order to improve the performance of spectrum sensing and make the spectrum sensing technology more suitable to the CR network, in this paper, we've made research on the local spectrum sensing and the cooperative spectrum sensing base on the energy detection.Firstly, against the influence of the noise uncertainty on the traditional energy detection, we've proposed an improved algorithm based on the double adaptive thresholds. Next, we proposed a network model for cooperative spectrum sensing based on the clusters and divided the CR users into different clusters. According to this network model, we've made a complete procedure of the cooperative spectrum sensing which includes two parts: the"Sensing in the cluster"and"Sensing among the clusters". During the period of"Sensing in the cluster", we've made use of the double adaptive thresholds algorithm, made a quantization of the sensing information between the double thresholds, and proposed a detailed quantitative program. After that, we have improved the method of the local spectrum sensing. We made the CR users with the higher SNR send their quantitative information processed, as the assistant decision information, to the CR users with the lower SNR in order to help them complete the local spectrum sensing together. We also made the research on the way of the transmission for the assistant decision information. Finally, we've made the cluster decision by the results of the improved local spectrum sensing, and then we got the global decision.Through simulation, we've verified that the model proposed is reasonable and the performance of our algorithm is excellent. The double adaptive thresholds algorithm improved the performance of the local spectrum sensing and compared to the traditional methods, the innovative strategy for the cooperative spectrum sensing also improved the detection performance greatly, especially in low SNR conditions.