李尚儒,,
谢显中
1.重庆邮电大学计算机科学与技术学院 ??重庆 ??400065
2.重庆邮电大学重庆市计算机网络与通信技术重点实验室 ??重庆 ??400065
基金项目:国家自然科学基金(61471076, 61601070)，重庆市基础与前沿研究计划(cstc2016jcyjA0455)，重庆市基础研究与前沿探索项目(cstc2018jcyjAX0432)，重庆邮电大学博士启动基金(A2015-16)
详细信息
作者简介:马彬：男，1978年生，教授，研究方向为异构无线网络、认知无线电网络等
李尚儒：男，1993年生，硕士生，研究方向为异构无线网络
谢显中：男，1966年生，教授，博士生导师，研究方向为无线和移动通信技术
通讯作者:李尚儒 lishangru93@163.com
中图分类号:TN915计量
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被引次数:0
出版历程
收稿日期:2018-05-30
修回日期:2019-01-30
网络出版日期:2019-02-25
刊出日期:2019-05-01
An Adaptive Vertical Handover Algorithm Based on Artificial Neural Network in Heterogeneous Wireless Networks
Bin MA,Shangru LI,,
Xianzhong XIE
1. Institute of Computer Science and Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China
2. Chongqing Key Laboratory of Computer Network and Communication Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61471076, 61601070), The Foundation and Advanced Research Program of Chongqing (cstc2016jcyjA0455), The Foundation Research and Advanced Exploration Project of Chongqing (cstc2018jcyjAX0432), The Doctoral Start-up Fund of Chongqing University of Posts and Telecommunications (A2015-16)
摘要
摘要:针对当前基于人工神经网络的垂直切换算法(ANN-VHO)，存在业务自适应性差和计算复杂度高的问题，该文提出一种基于人工神经网络的自适应垂直切换算法。首先，根据终端获取到的接收信号强度(RSS)，采用阈值判断的方法，遴选出候选网络集；其次，根据该文划分的不同业务类型，对参数进行自适应选择和归一化；再次，把选择的参数输入人工神经网络，判决出候选网络集中最佳的接入网络。最后，实验结果表明，该算法能根据用户的业务类型合理地选择切换网络，降低切换阻塞率，同时降低算法的时间复杂度。
关键词:异构无线网络/
业务类型/
自适应选择/
神经网络
Abstract:Current research on Vertical HandOver algorithm based on Artificial Neural Network (ANN-VHO) has a poor service adaptability and high computational complexity. Considering this problem, an adaptive vertical handover algorithm based on artificial neural network is proposed. Firstly, according to the Received Signal Strength (RSS) obtained by the terminal, a method of thresholding is used to select a candidate network set. Secondly, in terms of the different types of services classified in this paper, the parameters are normalized and adaptively selected; Thirdly, the selected parameters are input into the artificial neural network to choose the best access network from the candidate network. Finally, the experimental results show that the algorithm can reasonably select the handover network according to the user's service type, reduce the handover blocking rate and lower the time complexity of the algorithm.
Key words:Heterogeneous wireless networks/
Type of service/
Adaptive selection/
Neural network
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