Research On Vertical Handoff Algorithm For Controlling Frequent Handover In Cognitive Heterogeneous Wireless Network

With the rapid development of mobile terminals and wireless communication technologies,the next-generation wireless network will be composed of multiple wireless access technologies,which provide multiple access methods and support terminal seamless roaming.In order to make full use of free spectrum resources,this thesis studies the vertical handover problem in the cognitive heterogeneous wireless network composed of cognitive radio networks and ultra-dense heterogeneous wireless networks.In the cognitive heterogeneous wireless networks,how to effectively control frequent handovers,improve user service stability,and improve user service quality has become a hot issue in academic research.This thesis aims to improve the quality of user service.First,combining the user side and the network side,a vertical handover algorithm based on the gray relation analysis and the bipartite maximum matching algorithm is proposed;secondly,different types of services are distinguished according to the user's needs for service quality.For service type,a vertical handover algorithm based on neural network and grey relation analysis method is proposed.Both algorithms take into account the dynamic characteristics of cognitive radio networks,reduce the network handover rate,and improve the quality of service for.The main work of this thesis is as follows:1.In the cognitive heterogeneous wireless network,the frequent handover becomes more and more serious due to the dynamic characteristics of the cognitive radio network.A vertical handoff algorithm based on gray relation analysis and bipartite maximum matching is proposed.The main content of the algorithm is as follows: on the user side,the gray relation analysis method is used to sort cognitive heterogeneous wireless networks;on the network side,the results of the user-side's network ranking are used,and the maximum matching algorithm of the bipartite graph is used to achieve matching between user and network.The simulation results show that the algorithm can effectively control frequent handovers,improve network throughput and the quality of service,and achieve load balancing at the same time.2.Aiming at the problem of terrible user service stability in cognitive heterogeneous wireless networks,an adaptive network selection algorithm based on artificial neural networks and gray relation analysis is proposed.The main content of the algorithm is as follows: first,neural network algorithm is used to predict the arrival time of the main users of each candidate network and the future load status in cognitive radio networks;secondly,different service types are divided according to user needs.AHP and CRITIC algorithms are respectively used to calculate the subjective weights and the objective weights;finally,the gray relation analysis method is used to handover which will switch to the network with the highest score.Simulation results show that the algorithm can effectively improve user service stability and quality of service,improve network throughput,reduce network handover rate and handover blocking rate.