异构无线网络中的SON用例之间的协同管理研究
发布时间：2019-02-17 13:18
【摘要】：在异构无线网络中,逐步呈现出新特性,如异构性、复杂性、动态性以及开放性等,从而使得用户对无线网络所提供的服务质量QoS(Quality of Service)更加敏感,异构网络必须动态地适应用户的业务需求,才能够保证良好的用户体验。传统方法对网络进行优化及故障治愈将增大运营开销,为了使网络配置、自主优化的流程更加智能,从而部署为快速动态调整、高效运维的异构无线网络,SON(Self-Organized Network)概念被引入LTE网络中。自组织网络SON可以自主操作,控制和优化管理网络,设计一种自组织管理的网络运维机制已经成为未来异构网络研究的热点问题。然而,不同的SON功能用例间存在关联,当根据不同的优化目标进行优化时,SON功能之间产生冲突,从而影响网络性能,因此合理协调各个SON功能用例,进行冲突避免,从而实现多个SON功能的一致性管理和协同管理,有效改善网络性能是当前重要的研究内容。本文主要研究异构无线网络中,根据关键性绩效指标KPI(Key Performance Indicators),研究多个SON功能用例间的冲突避免及协同性管理,优化网络性能。首先,基于逻辑回归理论研究了网络中的关键性指标KPI与无线网络资源管理参数RRM(Radio Resource Management)之间的关系,构建了KPI-RRM映射模型,为SON用例优化提供优化基础准则。通过动态调整RRM参数,运行对应的SON功能,使网络达到指定的KPI指标,或者根据特定的RRM参数,反向调整KPI指标,满足网络运维需求。文章进一步研究了同一时间尺度下的多个SON功能用例冲突避免问题,重点分析了移动负载均衡和移动鲁棒性优化两个SON功能间的冲突解决策略。最后,提出了多时间尺度下的多个SON功能协同管理机制从而实现网络节能的优化策略,通过对异构网络中的基站运行协同合作机制,将网络节能问题分解成基站模式组合优化和网络负载均衡两个子问题求解,动态地对异构网络进行调整,控制网络中基站的开启和关闭,减少能耗,提升能量利用效率,实现网络节能。在本文中,通过实时检测网络状况(如网络负载)及相应关键性技术指标KPI(掉话率、阻塞率等),运行相应的优化机制,从而实现网络优化。
[Abstract]:In heterogeneous wireless networks, new features are gradually presented, such as heterogeneity, complexity, dynamics and openness, which makes users more sensitive to the quality of service (QoS (Quality of Service) provided by wireless networks. In order to ensure a good user experience, heterogeneous networks must dynamically adapt to the business needs of users. The traditional method of network optimization and fault cure will increase the operation cost, in order to make the network configuration, the process of self-optimization is more intelligent, so it can be deployed to the heterogeneous wireless network with fast dynamic adjustment and efficient operation and maintenance. The concept of SON (Self-Organized Network) is introduced into LTE networks. The self-organizing network (SON) can operate, control and optimize the network independently. The design of a self-organized network operation and maintenance mechanism has become a hot issue in the future heterogeneous network research. However, there are correlations among different SON function cases. When the SON function is optimized according to different optimization objectives, there will be conflicts between the SON functions, which will affect the network performance. Therefore, each SON function case can be coordinated reasonably to avoid the conflicts. In order to realize the consistency management and collaborative management of multiple SON functions and effectively improve the network performance is an important research content at present. This paper mainly studies the conflict avoidance and collaborative management among multiple SON function cases according to the key performance index (KPI (Key Performance Indicators),) in heterogeneous wireless networks, and optimizes the network performance. Firstly, based on the logical regression theory, the relationship between KPI, a key parameter in the network, and the resource management parameter (RRM (Radio Resource Management) in wireless network is studied, and the KPI-RRM mapping model is constructed, which provides an optimization criterion for the optimization of SON use cases. By dynamically adjusting the RRM parameters and running the corresponding SON function, the network can reach the specified KPI index, or adjust the KPI index in reverse according to the specific RRM parameters to meet the network operation and maintenance requirements. In this paper, the conflict avoidance problem of multiple SON function cases at the same time scale is studied, and the conflict resolution strategies between mobile load balancing and mobile robustness optimization are analyzed. Finally, this paper proposes a multi-function cooperative management mechanism of SON in multi-time scale to realize the optimization strategy of network energy saving. By running the cooperative cooperation mechanism of base stations in heterogeneous networks, a cooperative mechanism is proposed in this paper. The problem of network energy saving is decomposed into two sub-problems: the combination optimization of base station mode and network load balance, and the dynamic adjustment of heterogeneous network is carried out to control the opening and closing of base station in the network, to reduce energy consumption, and to improve energy utilization efficiency. Realize network energy saving. In this paper, the network optimization is realized by real-time detecting the network condition (such as network load) and the corresponding key technical index, KPI (drop rate, blocking rate, etc.), and running the corresponding optimization mechanism.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN92
[Abstract]:In heterogeneous wireless networks, new features are gradually presented, such as heterogeneity, complexity, dynamics and openness, which makes users more sensitive to the quality of service (QoS (Quality of Service) provided by wireless networks. In order to ensure a good user experience, heterogeneous networks must dynamically adapt to the business needs of users. The traditional method of network optimization and fault cure will increase the operation cost, in order to make the network configuration, the process of self-optimization is more intelligent, so it can be deployed to the heterogeneous wireless network with fast dynamic adjustment and efficient operation and maintenance. The concept of SON (Self-Organized Network) is introduced into LTE networks. The self-organizing network (SON) can operate, control and optimize the network independently. The design of a self-organized network operation and maintenance mechanism has become a hot issue in the future heterogeneous network research. However, there are correlations among different SON function cases. When the SON function is optimized according to different optimization objectives, there will be conflicts between the SON functions, which will affect the network performance. Therefore, each SON function case can be coordinated reasonably to avoid the conflicts. In order to realize the consistency management and collaborative management of multiple SON functions and effectively improve the network performance is an important research content at present. This paper mainly studies the conflict avoidance and collaborative management among multiple SON function cases according to the key performance index (KPI (Key Performance Indicators),) in heterogeneous wireless networks, and optimizes the network performance. Firstly, based on the logical regression theory, the relationship between KPI, a key parameter in the network, and the resource management parameter (RRM (Radio Resource Management) in wireless network is studied, and the KPI-RRM mapping model is constructed, which provides an optimization criterion for the optimization of SON use cases. By dynamically adjusting the RRM parameters and running the corresponding SON function, the network can reach the specified KPI index, or adjust the KPI index in reverse according to the specific RRM parameters to meet the network operation and maintenance requirements. In this paper, the conflict avoidance problem of multiple SON function cases at the same time scale is studied, and the conflict resolution strategies between mobile load balancing and mobile robustness optimization are analyzed. Finally, this paper proposes a multi-function cooperative management mechanism of SON in multi-time scale to realize the optimization strategy of network energy saving. By running the cooperative cooperation mechanism of base stations in heterogeneous networks, a cooperative mechanism is proposed in this paper. The problem of network energy saving is decomposed into two sub-problems: the combination optimization of base station mode and network load balance, and the dynamic adjustment of heterogeneous network is carried out to control the opening and closing of base station in the network, to reduce energy consumption, and to improve energy utilization efficiency. Realize network energy saving. In this paper, the network optimization is realized by real-time detecting the network condition (such as network load) and the corresponding key technical index, KPI (drop rate, blocking rate, etc.), and running the corresponding optimization mechanism.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN92
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