Analysis And Modeling Of Heterogeneous Wireless Network Service Performance

With the rapid development of mobile communication technology,people's demand and dependence on communication networks are increasing.At the same time,people's requirements for the quality of various services provided by communication networks are increasing.Traditional network architectures have been unable to withstand the pressure of increasingly complex networks,and heterogeneous wireless networks have emerged at the historic moment.The network topology becomes more and more complex,which poses certain challenges to the analysis and research on the quality of service of heterogeneous networks.Aiming at this problem,analysis and modeling in service performance of heterogeneous wireless networks has been accomplished,and certain results in terms of theoretical algorithms and different practical application scenarios are shown in this thesis.The main contributions are as follows:(1)Aiming at the high-load conditions of cells that may occur in heterogeneous wireless networks,the classification and prediction of high-load cells is studied in this thesis.An improved k Nearest Neighbor(kNN)algorithm based on distance threshold is proposed.Based on this algorithm,high-load cells in actual scenarios are predicted.Finally,the prediction results are visualized on the actual map.The experimental results show that compared with the traditional kNN algorithm,the improved algorithm can significantly reduce the calculation amount required by the algorithm,reduce the running time,and improve the prediction efficiency for high-load cells.(2)For the research of user satisfaction prediction,firstly,the accuracy of satisfaction prediction is compared between two types of models based on correlation analysis and causality analysis.And the next step is to focus on how to find the QoS that has the greatest impact on user satisfaction.The original causal analysis algorithm is improved in parameter solution to make the model has better performance.Simulation results show that the improved algorithm can accurately find the QoS that has the greatest impact on user satisfaction,and help operators improve users'satisfaction as much as possible under the premise of limited operation and maintenance resources.(3)Aiming at the research of network service quality modeling based on user satisfaction,especially in the heterogeneous network environment of macro and micro base stations,a novel algorithm based on long short term memory(LSTM)and Convolutional Neural Network(CNN)algorithm is proposed in this thesis.It is designed for targeted implementation of QoS and QoE mapping model construction in heterogeneous networks of macro and micro stations.The simulation results show that the model has obvious advantages over traditional mapping models in the accuracy of mapping in heterogeneous network scenarios with different simulation parameters.