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3G/WLAN网络中基于终端移动与业务认知的动态负载均衡机制
  • 期刊名称:计算机学报
  • 时间:0
  • 页码:1569-1579
  • 分类:TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构:[1]清华大学电子工程系,北京100084, [2]北京交通大学电子信息工程学院,北京100044
  • 相关基金:本课题得到国家“九七三”重点基础研究发展规划项目基金(2007CB307105)、国家自然科学基金(60903150)资助.
  • 相关项目:未来互联网的认知服务与感知路由基础研究
中文摘要:

下一代无线移动通信要求异构网络之间相互协作,3G蜂窝网与WLAN之间的密切合作将为用户提供无缝的业务链接与多样化通信服务.终端移动与业务时空分布的不均衡性是影响网络性能的重要因素,3G/WLAN网络中业务负载均衡策略是提高网络整体性能的有效途径.首先,文中提出了一种新的基于终端移动与业务认知的动态负载均衡(DLB—MSA)机制,该机制重点关注业务潜在用户,通过动态优化的联合业务接入控制,使得每个无线接入点的业务量与其通信资源保持匹配关系,实现网络的负载均衡.然后,为了探讨终端移动对无线网络通信性能的影响,文中创建了一种新的基于2维终端速率分类与状态转换的移动模型,并且给出了相应的Markov分析方法.该模型能够更准确地反映用户移动规律,通过比较简单的参数学习来逼近3G/WLAN网络中终端的群体移动特征.最后,文中基于该模型对DLB-MSA机制进行性能评估,通过仿真验证了该机制的有效性.

英文摘要:

In the next generation of wireless mobile communications, it can be visualized that heterogeneous networks including 3G cellular networks and WLAN are demanded to co-operate user-friendly in a seamless manner. Service load balance among radio access points is very important for improving performance of integrated 3G/WLAN networks. One of the key factors for network load balance is mobility of users, which causes disproportional distribution of services in time and space domain. In the paper, a new dynamic load balance scheme based on mobility and service awareness (DLB-MSA) is proposed, aiming to match traffic load with radio resource configurations at each radio access point. The DLB-MSA scheme is paid attention to underlying users, whose velocity and service arrival probability are main parameters for joint session admission control in 3G/WLAN networks. Furthermore, a new mobility model is raised and analyzed by Markov theory for performance analysis of heterogeneous networks. It is based on bi-dimensional velocity classification and state transition, which is fit for mobility simulation of both vehicle users and pedestrian users. Finally, based on this mobility model, scheme is verified by numerical results. It not only reduces call block throughput of 3G/WLAN networks, but also minimizes vertical han performance of DLB-MSA ing probability and improves doff rate.

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