Research On Resource Optimization Algorithm For Wireless Cognitive Network

The future communication is changeable anytime, while its remarkable characteristics can be concluded to meet business growth and the relevant growth of broadband. Reflected in the wireless mobile communication field, make wireless mobile communication network will face challenges, such as more intense competition of the network resources and different eterogeneous wireless network to work together to meet the needs of the business.The research includes basic theory and concrete realization link technology, etc. Different from the traditional network, for its unique cognition of the wireless network, traditional Service Quality (Quality of Service, QoS) theory cannot be used in the field anymore. Currently, it lacks systemic research on QoS technology, such as the standardized definition of parameters, guarantee mechanism and system framework. In this paper, the premise of resource parameter optimization is the question of the study that under how to avoid the authorized user of the disturbance.The research works of the paper are illustrated as follows:To build up QoS guarantee cognitive mechanism and system structure, and set up the standardized description QoS parameters based on different layer layered definition of, and finally emphasizes the QoS decision-making process;To choose three parameters of used bandwidth, power consumption and transmission rate as resources optimization objective parameter, and prove the rationality of the selection of three parameters using the maximum and minimum analysis method, and finally it present the mathematical model of the multi-objective problem.To fuse cloning genetic algorithm and neighborhood search algorithms’ characteristics, through the modification of fitness function and clonal selection, adaptive mutation operator, it put forward a kind of adaptive neighborhood search cloning genetic algorithm for the multi-objective evolutionary issues, and after comparison the simulation of the algorithm with classical algorithm NSGA-II, the advantages are proved.