Research On Spectrum Sensing And Power Optimization Techniques Of Cognitive Radio Networks

Cognitive radio realizes the utilization of spectrum holes, which has alleviated the embarrassment between the spectrum resource scarcity and increasing demands. Cognitive radio networks using cognitive radio techniques realize spectrum sharing, so that the cognitive user will not cause harmful interference to the primary user. However, the specific network applications need accurate spectrum sensing algorithms to improve the system detection performance, and ensure the spectrum utilization. Therefore, this thesis researches the cognitive radio spectrum sensing and power optimization schemes to improve the system overall performance.This thesis takes spectrum sensing of cognitive radio sensor network as the detection performance research case of cognitive radio network applications, and solves the problems of multiple antenna power optimization of cognitive radio network. The main research work is summarized as follows:1) To solve the problem of the network detection performance degradation caused by the different collaborative mechanisms between the sink node and sensor nodes, this thesis presents a multiple antenna power allocation algorithm based on clustering and sink node weight multi-objective optimization. The spectrum sensing process is divided into three different phase for the local spectrum sensing, sensor nodes clustering, weight vector optimization and date fusion strategy in the final global decision, which forms the sink nodes location optimization and clustering scheme based on minimum of Within-Cluster Sum of Squares. The thesis further analyzes the system parameters’ influence degree to the promotion of cognitive user detection performance, and experimental results show that the proposed algorithm can effectively improve the cognitive system detection performance in cognitive radio sensor networks.2) A multiple antenna power allocation algorithm under multiple antenna power allocation is proposed in this paper. In order to avoid harmful interference to the primary user, the algorithm adaptively adjusts the transmission power of transmitter cognitive user based on primary user’s presence or not. Simultaneously, in order to maximize the system achievable rate, the optimal power allocation strategies among transmitter antennas are derived under the primary interference power constraint and a given target detection probability in this thesis. The experimental results show that the optimal power spectrum sharing model improves the efficient spectrum utilization and the transmission rate of cognitive users compared with the traditional spectrum sharing algorithms.