Research On The Key Technology Of Decision Making And Management In Cognitive Radio Networks

Decision making and Management are key technologies in cognitive radio networks, which are of widespread concern and have broad application prospects. This dissertation is supported by the 863 Project and other funding, and has important theoretical and practical significance.Based on the comprehensive study on the basic principles of resource decision making and management in cognitive radio networks, the main contributions and innovative ideas in the dissertation include:For the problem that single user tend to be low efficient in spectrum sensing in low SNR environments, this paper proposed a collaborative beamforming technique based on the distributed ad hoc network. While this method does not require any prior information, it can effectively suppress noise and improve the received SNR gain by taking advantage of the directional gain of beamforming. Through the analysis based on the energy detector, this method is proved to be effective in enhancing the efficiency of spectrum sensing.For the problem of spectrum allocation in cognitive radio networks, a spectrum decision making and allocation model which incorporates the AHP (Analytic Hierarchy Process) algorithm and the graph coloring theory is proposed. The model considers matching some factors of the spectrum, such as the spectrum available time, bandwidth, and delay, with different service characteristics to allocate the most appropriate spectrum resources to specific users. The combination of the two algorithms can refine the spectrum decision making and allocation mechanism, guarantee the utilization of both secondary users and the entire network, improve the network spectrum efficiency, and reduce the number of spectrum switches of secondary users. For the problem of power control in cognitive radio networks, this paper proposes a power control model based on the non-cooperative game theory. In this model a SIR-based cost function is devised, which takes into account the interference from primary users to secondary users. Simulation results show that the algorithm can improve the power efficiency, increase the capacity of secondary users in cognitive radio networks, and achieve faster convergence speed, well adapting to needs of fast and efficient power allocation in the environments of cognitive radio networks.For resolving the low efficiency of multi-objective optimization algorithms in cognitive engines, a binary quantum particle swarm optimization algorithm is proposed for cognitive engines. Due to the introduction of the quantum properties which can provide nonlinear and uncertain characteristics, the algorithm has faster convergence speed and higher accuracy, thus making up for the low efficiency of traditional optimization algorithms in seeking optimal solutions. The performance of the algorithm is verified through the OFDM system, and the simulation results show that the algorithm can play a favorable effect of multi-objective optimization, achieving the goal of optimizing radio resource decision making.Finally, the content of the whole dissertation is summarized and several valuable research directions are also discussed.