LTE网络负载均衡技术研究

随着LTE网络的逐渐部署,LTE网络的优化问题目趋重要。本文在分析LTE网络SON（SelfOrganizingNetworks）技术的基础上,重点探讨了针对LTEN络的负载均衡技术。     

LTE中一种结合负载均衡和自适应门限的切换算法

LTE正成为通信、信息、医疗以及汽车等众多领域革新的源动力.文章基于对系统整体性能的优化,结合负载均衡和自适应门限,提出了ATBLB算法,旨在动态调节系统自优化,从而更大程度地改善系统的整体性能.结果表明:与WPHPO算法和WPLBHO算法相比,ATBLB算法在保证切换失败率基本不变的情况下,使系统稳定后的掉话率分别降低了35％和15％;乒乓切换率分别降低了8％和3％;从而使整体切换性能HP提高了40％和15％.因此,ATBLB算法更能改善系统的整体性能,从而有利于LTE在其他领域的应用推广.     

自组织无线网络中的移动负载均衡技术

文章认为基于人工进行的网络优化方法难以适应未来网络发展需要,为此LTE系统引入了自组织网络技术。自组织网络技术涉及网络部署、运行的各个环节,其中移动负载均衡技术是自组织网络技术的一个关键技术。移动负载均衡技术通过用户在不同小区之间的切换实现负载的转移,从而实现负载的均衡分布,提高网络性能。针对很多移动负载均衡算法没有考虑用户的服务质量(QoS)要求,文章介绍了一种保证QoS要求的移动负载均衡算法。     

基于博弈的LTE负载均衡算法研究

针对LTE网络中小区之间负载不均衡的问题,提出了一种结合经济学中的博弈理论的解决方案.通过将博弈理论中的拍卖模型引入到LTE系统模型中,建立竞拍双方的效用函数,求出均衡解最大化其效用函数,从而合理地调整切换参数将热点小区的负载转移到目标小区.仿真结果证明了该算法的有效性,该方案可以合理有效地解决小区间负载不均衡问题、优化网络资源和提高资源利用率.     

LTE移动性负载均衡负载报告机制研究

移动性负载均衡(MLB)是LTE自组织网络(SON)的重要功能。在研究3GPP关于MLB负载测量信息及信息交互机制的基础上,针对基于物理资源块(PRB)负载触发的MLB功能与当前移动互联网业务模型之间匹配性不佳的问题,提出了一种基于现行交互标准的,结合PRB占用率及激活用户占比的综合负载评估及交互技术方案。     

LTE中权值μ对双层负载均衡的影响研究

LTE中关于负载均衡优化的一个重要方面是如何选择最优的目标小区。现有双层负载均衡方法在选择目标小区时将权值设为固定值,忽略了具有不同QoS需求的用户及其移动速度,这对系统的优化性能有一定影响。该文通过仿真测试,加入对具有不同QoS需求的用户及移动速度的考虑,对权值的不同取值及该取值对系统性能的影响进行研究。通过系统仿真,并与设置固定权值的双层负载均衡方法的系统进行性能比较,可知权值的设定对于网络性能有着一定的影响。     

LTE多载波负载均衡研究与应用

当前LTE网络流量快速增长,资源需求也随之快速增长,使得LTE网络资源短缺成为普遍问题。除此之外,由于用户的移动与业务分布在区域上具有不均衡性,导致资源局部紧张,部分用户体验下降。再有,在异频组网中,当小区处于高负载状态时,能将高负载小区的UE转移到低负载小区中,提升业务接入成功率的同时,改善用户业务感受,提高整个资源利用率。本文对移动负载平衡的算法流程及性能展开研究,重点研究了交叠场景的负载均衡方案,并在LTE流量暴涨、投资有限的情况下,进行优化方法的探索,解决部分高负荷的问题。     

3GPP LTE系统中小区负载均衡机制的研究

第三代合作伙伴计划(3GPP)长期演进(LTE)系统与上一代无线通信网络相比,在基站传输速率和小区容量上都有了极大的提高,相应地对网络无线资源管理功能也提出了更高的要求.文章探讨了无线资源管理中的一个重要方面:负载均衡的问题.首先分析了网络中小区可能发生过载的原因,并讨论了传统的基于小区切换的解决方法可能存在的风险.随...     

LTE异频负载均衡算法在高负荷场景的应用分析

随着中国移动LTE网络规模不断扩大和网络覆盖质量不断提升,LTE网络用户规模逐年递增,流量热点区域的"高负荷"成为影响用户感知的不利因素,因此亟需一种更为精细化的优化手段来解决问题。文章主要对LTE出现的局部高负荷场景进行分析研究,针对目前主流的F+D双层网及双载波形式进行深层次优化,总结出一套高负荷场景下的负载均衡策略参数,最大程度地提升LTE用户感知及相关关键指标。     

移动性负载均衡(MLB)在校园优化中的应用分析

随着LTE用户数的飞速发展和用户潮汐效应,部分场景下忙时小区用户数或PRB利用率已接近饱和,而周边小区资源利用率却很低,如何均衡特殊场景下同覆盖或存在重叠覆盖区域的小区间的负载是一个极有意义的课题。文章以武汉校园场景为例,探讨LTE小区移动负载均衡~[1]参数优化。     

LTE网络中多目标优化的动态负载均衡算法

小区间动态负载均衡算法的优劣对网络性能有很大的影响,现有算法大都顾此失彼,难以保证网络关键性能指标(如呼叫阻塞率等)达到最佳。为解决这个问题,该文将负载均衡问题建模为多目标优化问题,分别对不同服务质量要求用户建模其相应目标优化函数,对有服务质量要求用户的目标函数是负载均衡指示函数和网络平均负载函数的联合优化函数,而对没有服务质量要求用户(BE用户)的目标函数是所有BE用户吞吐量的总效用函数,并将小区可用资源数和用户服务质量要求作为约束条件。另外,考虑到在实际系统中运行的计算复杂度问题,提出一种复杂度较低的分布式负载均衡算法。包括资源调度策略,用户切换条件和呼叫接入控制。仿真结果表明,该文算法可达到较好的负载均衡效果,能有效降低有服务质量要求用户的呼叫阻塞率,提高网络资源利用率。     

LTE中一种基于负载均衡的自优化切换算法

在3GPP LTE自组织网络（SON）中,面向整体切换性能的自优化算法明显优于面向个别性能的算法.本文针对EWPHPO算法无法对小区负载进行动态调整,也无法对单个过载小区的切换参数进行及时调整,从而导致掉话率等指标不能得到有效控制的问题,提出了兼顾整体切换性能和负载均衡的WPLBHO算法.通过仿真并与EWPHPO算法在相同环境下的仿真结果进行比较,我们的算法在保持切换失败率基本不变的情况下,使掉话率降低约20%、乒乓效应降低约5%,从而使系统整体切换性能（HP）提升约25%;同时,使系统平均过载时间下降近50%.因此我们的算法使系统整体性能得到了明显改善.     

A Differential Game Model of Automatic Load Balancing in LTE Networks

In this paper, load balancing problem is investigated and a new game theory automatic load balancing scheme in LTE networks is proposed based on non-cooperative differential game. Optimal load allocated to each cell is formulated as a Nash Equilibrium in this paper. It is proved that the non-cooperative differential game algorithm is applicable and the optimal load solution can be achieved. Simulation results show that system capacity is increased and load unbalancing caused inappropriate load allocation is decreased.     

Joint Load Balancing of Radio and Transport Networks in the LTE Systems

Load Balancing (LB) is an important mechanism often used in the cellular networks to offload the excessive traffic from high-load cells (hot spots) to low-load cells. Extensive studies have shown that the system level performance with LB can be greatly enhanced in terms of e.g. higher overall system throughput, lower user blocking ratio and better QoS (Quality of Service) for the users at the cell boundaries. However, so far the conventional LB solutions are mainly focusing on Radio Networks (RN), and little consideration of the impact from Transport Networks (TN) has been taken into account in the LB mechanisms. This paper proposes a joint load balancing framework for the Long Term Evolution system. The proposed LB takes into consideration the joint impact of RN and TN by combining the network-wide utilities to achieve overall network optimizations. In this paper, we suggest different utilities for GBR and nGBR service categories. For each service category, the proposed utilities consider multiple QoS classes with different services (e.g. VoIP, video and FTP) and aim to guarantee their individual QoS requirements. The presented numeric results show that the proposed mechanism can outperform the conventional RN-only LB, especially for the situation that the TN last mile link is the bottleneck and its deploy-ment is heterogeneous.     

基于临界触发的LTE负载均衡算法

考虑LTE用户信噪比,借助物理资源块的服务能力推算小区负载的变化状态,提出的CTLB算法能够动态地根据每个小区的负载,自动计算需要进行用户转移的小区。引入均衡因子,使所有小区最终达到预先设定的收敛目标,从而有效利用网络资源。最后,通过MATLAB搭建了一个仿真平台,验证算法的收敛性能、过载用户数及小区吞吐量的变化情况。     

Reliability and Mobility Load Balancing in Next Generation Self-organized Networks: Using Stochastic Learning Automata

Self-organizing networking (SON) is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster. Most current self-organization networking functions apply rule-based recommended systems to control network resources which seem too complicated and time-consuming to design in practical conditions. This research proposes a cognitive cellular network empowered by an efficient self-organization networking approach which enables SON functions to separately learn and find the best configuration setting. An effective learning approach is proposed for the functions of the cognitive cellular network, which exhibits how the framework is mapped to SON functions. One of the main functions applied in this framework is mobility load balancing. In this paper, a novel Stochastic Learning Automata has been suggested as the load balancing function in which approximately the same quality level is provided for each subscriber. This framework can also be effectively extended to cloud-based systems, where adaptive approaches are needed due to unpredictability of total accessible resources, considering cooperative nature of cloud environments. The results demonstrate that the function of mobility robustness optimization not only learns to optimize HO performance, but also it learns how to distribute excess load throughout the network. The experimental results demonstrate that the proposed scheme minimizes the number of unsatisfied subscribers (N_us) by moving some of the edge users served by overloaded cells towards one or more adjacent target cells. This solution can also guarantee a more balanced network using cell load sharing approach in addition to increase cell throughput outperform the current schemes.

     

Opposition Based Artificial Flora Algorithm for Load Balancing in LTE Network

In recent years, telecommunication is progressed due to the development of Long-Term Evolution (LTE) standards. This LTE network provides high-speed wireless communication for mobile devices and satisfies the requirements of customers from multi-cells. However, due to the congestion of mobile devices, each cell in the network may get overloaded. So, load balancing is the main challenge to the LTE network for reducing congestion or load in the cell. For load balancing, the optimal cell selection method is presented in this paper. Initially, the load factor of each cell is estimated. Then the load factor is compared with the predefined threshold load value. After the comparison, the heavy loaded cell handover the users to the optimal cell or cell with minimum load. This optimal cell is selected with the Opposition Based Artificial Flora (OAF) algorithm. Simulation results show that the proposed approach decreases 6% of the call blocking ratio (CBR) and 14% of Call Dropping Ratio (CDR) than the previous approaches.