Research On Handoff Algorithm Based On Environment Prediction In Ultra-dense Heterogeneous Wireless Networks

In recent years,with the demand of mobile terminals for network services to the trend of high quality and diversification.Meanwhile,the ultra-dense heterogeneous wireless network,which is composed of 5G ultra-dense network and traditional heterogeneous wireless network,has the characteristics of super high density,high complexity and strong heterogeneity.How to improve the network service quality of terminals in the ultra-dense heterogeneous wireless network environment has become a key and difficult issue in the current academic and industrial circles.This thesis use the method of terminal location prediction to analyze the change of access network's quality when terminal's position changes,in order to reduce the number and delay of network handoff to improve the overall performance of the network.The main work of this thesis is as follows:1.In the ultra-dense heterogeneous wireless network composed of dense heterogeneous cellular network and wireless local area network,the vehicle terminal with variable speed will face more frequent handoff,resulting in poor quality of service for users.This chapter aims at this problem.Firstly,the Gauss Markov model is used to predict the location of the vehicle at the next moment,the candidate network set that meets the quality of service is selected,and intersected with the current candidate network set.Secondly,if the current access network is not in the intersection,the variable step size firefly algorithm is used to find the best network.Thirdly,if the handover fails due to the error prediction,the terminal will be migrated to the macro cell to ensure the continuity of communication.Finally,the simulation results show that the proposed algorithm reduces the number of handoff,improves the quality of service and network throughput of users.2.In view of the above work,without considering the increase of candidate networks,mobile terminals will face more serious handover delay,resulting in poor user service quality.Therefore,the similarity of the motion trajectory is used to predict the location of the terminal,and whether trigger handoff is determined according to the position.and then the network candidate set with high service quality is generated in advance,A location prediction algorithm based on historical information is proposed.Outlier algorithm and K-means clustering algorithm are combined to analyze historical data and find out the location of network obstacle points for network handoff decision.Before that,the historical data is classified by time and space.Then,in the historical data,the movement trajectory most similar to the current terminal movement trajectory is found to predict the location of the terminal at the next moment.In order to reduce the number of track finding and improve the accuracy of prediction,K-means clustering algorithm is used to further classify the data.Finally,whether the handoff is triggered is determined according to the location.If the handoff is triggered,the network candidate set generated before the handoff triggers will be intersected to generate a high-quality network candidate set.The optimal access network is selected by using the interval multi-attribute decision algorithm of binary connection number.Simulation results show that the algorithm in this chapter reduces the handoff delay,improves the prediction accuracy and network throughput.