Research Of Key Technologies For Cognitive Radio Networks Based On Intelligent Algorithm

With the rapid development of wireless communication technology, Wireless Networks plays an increasingly important strategic role in the country’s society-economic development and has been applied to penetrate into various application areas of applications of the community. With the growing demand for wireless communication services, spectrum resource is becoming increasingly scarce. Cognitive Radio Networks is a new solution to solve the contradiction between supply and demand of the spectrum resource. In Cognitive Radio Networks, unlicensed users can opportunistic access for idle spectrum and share spectrum resources with the licensed users when they don’t affect the normal communication of the licensed user. To achieve the dynamic spectrum sharing of resources, the unlicensed users should not to cause harmful interference to licensed users, and the spectrum resources should be effective management. Therefore, reliable spectrum sensing and reasonable spectrum allocation are two key technologies to improve the coefficient of spectrum utilization in cognitive radio networks.Because the intelligence of CRN and the spectrum opportunity use, there are many parameters in the spectrum resource sharing issue. And the spectrum resource sharing issue will become the non-convex optimization problem after mathematical modeling. Intelligent optimization algorithm can effectively solve such problems because of it’s strong global optimization ability. Harmony Search Algorithm and Bacteria Foraging Optimization Algorithm are two new intelligent algorithms that be proposed in recent years. They have been demonstrated the strong optimize ability when be applied in function optimization, data processing and other engineering fields. In this paper, Harmony Search Algorithm and Bacteria Foraging Optimization Algorithm are researched. This paper proposed a new Chaotic Harmony Search Algorithm to solve the optimal linear cooperative spectrum sensing problem and proposed a quantum variation based on the improved Binary Bacterial Foraging Optimization Algorithm to solve spectrum allocation problem for CRN. This paper has carried on the beneficial exploration on the application of intelligent algorithm in the field of CRN. The main contents of this paper including the following:1. Introduced two key technologies of CRN:spectrum sensing and spectrum allocation, especially the energy detection model based cooperative spectrum sensing algorithm and the model of spectrum allocation algorithm based on graph coloring.2. The Harmony Search Algorithm and its application in optimal linear CRN.1) Chaotic harmony search algorithm. Against the shortcoming of Harmony Search Algorithm with update only one feasible solution and easy to fall into local optimization, this paper used the concept of grouping co-evolution and Logistic equation iteration of the chaotic system to generate the initial population and chaotic disturbance to improve better solutions, and finally tested the performance of this algorithm with simulation.2) Optimal linear Cooperative spectrum sensing based on Chaotic Harmony Search Algorithm. This paper researched the mathematical model of optimal linear cooperative spectrum sensing, then used chaotic harmony search algorithm to solve optimal weight vector, and through MATLAB simulation verified the effectiveness of the algorithm.3. Improved Binary Bacterial Foraging Optimization Algorithm and its application in spectrum allocation for CRN1) Improved Binary Bacterial Foraging Optimization algorithm. This paper introduced sigmoid function into the update strategy of the basic Bacterial Foraging Algorithm to solve0-1combinatorial optimization problems, and then used a simple probabilistic quantum algorithm mutation strategy to improve the performance of this algorithm.2) Improved Bacterial Foraging Algorithm based on spectrum allocation. This paper used the improved Binary Bacterial Foraging Algorithm to solve the problem of spectrum allocation based on graph coloring spectrum allocation model, and verified the effectiveness of the method by MATLAB simulation.