车辆异构网络中基于决策树的稳健垂直切换算法

马彬; 汪栋; 谢显中

doi:10.11999/JEIT161182

车辆异构网络中基于决策树的稳健垂直切换算法

doi: 10.11999/JEIT161182

基金项目:

国家自然科学基金(61601070, 61271259, 61301123, 61471076)，重庆市基础与前沿研计(cstc2016jcyjA0455, cstc2015 jcyjA40047)，重庆邮电大学博士启动基金(A2014-10, A2015-16)

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出版历程
- 收稿日期: 2016-11-03
- 修回日期: 2017-02-26
- 刊出日期: 2017-07-19

Robust Vertical Handoff Algorithm Based on Decision Tree in Vehicle Heterogeneous Network

Funds:

The National Natural Science Foundation of China (61601070, 61271259, 61301123, 61471076), The Foundation and Advanced Research Program of Chongqing (cstc2016jcyjA0455, cstc2015jcyjA40047), The Doctoral Start-up Fund of Chongqing University of Posts and Telecommunications (A2014-10, A2015-16)

摘要

摘要: 在带有噪声干扰的车辆异构网络中，针对当前基于决策树的垂直切换算法存在切换精准性不高的问题，该文详细分析并给出当前算法决策过程中存在的错误判决概率，提出一种基于决策树的稳健垂直切换算法。首先，采用卡尔曼滤波算法，根据网络属性的预测值和当前测量值，并结合它们各自的噪声偏差，做出更准确的网络属性估计。其次，针对少量网络属性值出现在判决门限附近的情况，提出概率阈值区间法，通过二次检测提高算法判决的准确性。仿真结果表明，所提算法提高了切换判决精准性和网络总吞吐量，降低了乒乓效应和切换失败率，并得到了与传统算法同阶的时间复杂度性能结果。
- 车辆异构网络 /
- 垂直切换 /
- 决策树 /
- 卡尔曼滤波
Abstract: In the vehicle heterogeneous network with noise and interference, the current vertical handoff algorithms based on decision tree have the problem of low handoff accuracy. In this paper, the decision processes of current algorithms are analyzed in detail and the formulation of false decision probability is given. Firstly, the Kalman filtering algorithm is employed to obtain the more accurate network attribute values according to the predicted values, the current values, and their noise deviations. Secondly, a probability threshold interval method is proposed to do a twice detection to the situation of the attribute value which is near the threshold. Simulation results show that the proposed algorithm can improve the accuracy of handoff decision and the total network throughput, and can also reduce the ping-pong effect and the failed handoff. Meanwhile, it still keeps the same-ordered time complexity with the traditional algorithms.
- Vehicle heterogeneous network /
- Vertical handoff /
- Decision Tree (DT) /
- Kalman filtering

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参考文献(14)

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