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异构无线网络中基于人工神经网络的自适应垂直切换算法

马彬 李尚儒 谢显中

引用本文: 马彬, 李尚儒, 谢显中. 异构无线网络中基于人工神经网络的自适应垂直切换算法[J]. 电子与信息学报, 2019, 41(5): 1210-1216. doi: 10.11999/JEIT180534 shu
Citation:  Bin MA, Shangru LI, Xianzhong XIE. An Adaptive Vertical Handover Algorithm Based on Artificial Neural Network in Heterogeneous Wireless Networks[J]. Journal of Electronics and Information Technology, 2019, 41(5): 1210-1216. doi: 10.11999/JEIT180534 shu

异构无线网络中基于人工神经网络的自适应垂直切换算法

    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
  • 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)

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。

English

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  • 图 1  判决算法流程图

    图 2  参数自适应选择

    图 3  神经网络结构

    图 4  异构无线网络系统模型

    图 5  算法时间开销

    图 6  会话类业务得分值

    图 7  3种类业务得分值

    图 8  切换阻塞率

    图 9  网络总吞吐量

    表 1  各参数的范围值

    业务带宽(kbps)时延(ms)抖动(ms)丢包率(×10–6)
    会话类30~1005~40
    交互类50~2701~100
    流类50~1000010~50
    后台类10~100010~1000
    下载: 导出CSV

    表 2  候选网络的参数值

    网络带宽(kbps)时延(ms)抖动(ms)丢包率(×10–6)
    LTE 310 48 9 28
    WLAN1 4100 105 38 9
    WLAN2 6900 180 67 1
    WLAN3 3400 50 17 1
    WLAN4 2300 60 11 20
    WLAN5 5600 91 12 16
    下载: 导出CSV
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文章相关
  • 通讯作者:  李尚儒, lishangru93@163.com
  • 收稿日期:  2018-05-30
  • 录用日期:  2019-01-30
  • 网络出版日期:  2019-02-25
  • 刊出日期:  2019-05-01
通讯作者: 陈斌, bchen63@163.com
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