{"title":"基于RBF方法的无所不在无线网络垂直切换决策","authors":"S. Kunarak","doi":"10.1109/PLATCON.2016.7456839","DOIUrl":null,"url":null,"abstract":"Next generation wireless networks are integrated the multiple wireless access technologies in order to provide the users with the best connection. The vertical handover decision algorithm is an important role to guarantee the seamless mobility in single mobile terminal. In this paper, we apply the radial basis function neural network (RBFNN) for the decision making process in vertical handover based on received signal strength, mobile speed and monetary cost metrics. The simulation results indicate that the proposed approach outperforms in reducing the unnecessary handover and connection dropping but increasing the grade of service with comparing the other two methods as threshold and back propagation neural network.","PeriodicalId":247342,"journal":{"name":"2016 International Conference on Platform Technology and Service (PlatCon)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Vertical Handover Decision Based on RBF Approach for Ubiquitous Wireless Networks\",\"authors\":\"S. Kunarak\",\"doi\":\"10.1109/PLATCON.2016.7456839\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Next generation wireless networks are integrated the multiple wireless access technologies in order to provide the users with the best connection. The vertical handover decision algorithm is an important role to guarantee the seamless mobility in single mobile terminal. In this paper, we apply the radial basis function neural network (RBFNN) for the decision making process in vertical handover based on received signal strength, mobile speed and monetary cost metrics. The simulation results indicate that the proposed approach outperforms in reducing the unnecessary handover and connection dropping but increasing the grade of service with comparing the other two methods as threshold and back propagation neural network.\",\"PeriodicalId\":247342,\"journal\":{\"name\":\"2016 International Conference on Platform Technology and Service (PlatCon)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Platform Technology and Service (PlatCon)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PLATCON.2016.7456839\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Platform Technology and Service (PlatCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PLATCON.2016.7456839","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vertical Handover Decision Based on RBF Approach for Ubiquitous Wireless Networks
Next generation wireless networks are integrated the multiple wireless access technologies in order to provide the users with the best connection. The vertical handover decision algorithm is an important role to guarantee the seamless mobility in single mobile terminal. In this paper, we apply the radial basis function neural network (RBFNN) for the decision making process in vertical handover based on received signal strength, mobile speed and monetary cost metrics. The simulation results indicate that the proposed approach outperforms in reducing the unnecessary handover and connection dropping but increasing the grade of service with comparing the other two methods as threshold and back propagation neural network.