考虑负载均衡和用户体验的垂直切换算法

在超密集异构无线网络中,针对城区交通高峰期,大规模车载终端短时间聚集性移动引起的网络拥塞问题,该文提出一种考虑负载均衡和用户体验(LBUE)的垂直切换算法。首先,引入网络环境感知模型预测网络未来的拥塞程度,并提出一个融合自组织网络的网络架构,缓解网络拥塞。其次,定义业务适应度和负收益因子,并提出一种基于秩和比(RSR)的自适应切换判决算法,为用户筛选出当前环境下满意度最高的目标网络。实验结果表明,该算法能够有效降低终端接入网络的阻塞率和掉话率,实现网络间负载均衡并提升用户体验。     

一种基于业务切换的垂直切换判决算法

在下一代异构无线网络环境中,为满足在异构无线网络架构下采用不同切换方式的垂直切换请求,我们提出一种基于业务切换的垂直切换判决算法,并设计了一种基于业务切换的代价函数。通过数学分析和在UMTS,WLAN融合的网络环境下对该方案的切换失败率的仿真分析,证明了该方案在支持终端的移动性、保障用户业务请求满意度以及缓解网络负载压力上相比传统基于代价函数的垂直切换判决算法更具优势。     

基于强化学习的异构网络垂直切换方法

网络切换技术不仅可以保证用户的网络连接,同时能够以较强的信号传输网络数据。网络切换技术的性能对网络服务质量(quality of service, QoS)的影响至关重要。然而,现有的切换算法多数存在严重的乒乓效应,这不仅造成网络资源的严重浪费,还会损害用户的QoS。为此,提出了一种基于强化学习的异构网络垂直切换方案,主要从触发切换、网络选择及判决切换等方面进行优化。在触发切换时将垂直切换考虑成必要切换和择优切换,通过Q-Learning（QL）算法在选择网络时优化垂直切换;然后以QoS为条件,在判决切换时加入驻留定时器,从多角度减少用户切换次数,降低乒乓效应对异构网络垂直切换的影响。仿真结果表明,基于强化学习的异构网络垂直切换方法可以在保证QoS的条件下有效减少用户切换网络的次数,短时间内频繁切换的情况也有所改善,降低了乒乓效应的影响。     

一种基于移动趋势量化的多属性垂直切换判决算法

由于对终端移动趋势的不明确,基站容易盲目发起切换,并导致较高的切换失败率。该文在LTE-WiMAX网络构成的异构无线网络环境下对现有的垂直切换算法进行了优化。该优化算法考虑了终端移动趋势,利用趋势量化参数来推断终端最终的目标区域,解决已有判决算法中存在的不必要切换过多的问题,提高切换成功率。在衰落信道下的计算机仿真结果表明,该优化算法可以减小切换中的切换失败率,从而提高网络的切换性能。     

异构网络垂直切换机制研究

异构网络中的垂直切换复杂于同构网络的水平切换,是一个多目标决策问题,涉及不同的网络参数,需要根据这些参数进行综合决策.文中提出了一种切换机制,终端自适应的调整网络接口开启间隔,以低能耗的方式监测RSS来发现链路状态良好的可用网络,并以综合QoS最佳判决函数选择目标网络,满足业务与用户需求.     

无线异构网络的垂直切换算法研究

以通用移动通信系统(UMTS)与无线局域网(WLAN)融合为例,研究无线异构网络的垂直切换算法。由于垂直切换算法应考虑因素的复杂性以及不同网络性能的各异性,有必要对现有算法作出归纳总结比较,并据此提出新的垂直切换算法。该算法以代价函数为基础,将稳定周期与切换目标网络和当前服务网络的效用函数联系起来,实现移动节点自适应的切换判决。提出的算法能有效减少不必要的切换,增强异构网络的适应性。     

基于区间标记判决的稳健垂直切换算法研究

在异构5G网络中,针对切换算法稳健性差引起的切换准确率低的问题,提出一种基于区间标记判决的稳健垂直切换算法.首先,引入中位值平均滤波法,获取更准确的网络参数.其次,基于参数波动特征分析,提出区间标记判决算法,保证候选网络筛选通过率和准确率的同时,提高区间标记判决算法的稳健性.再次,根据移动终端传输层需求,结合终端运动趋势,分别使用不同权值的效用函数获得最佳目标网络.最后,仿真结果表明,该算法能够有效提升切换触发和网络筛选的准确率,降低切换失败率和乒乓效应,提高系统吞吐量,并能够根据移动终端的需求选择最佳目标网络.     

异构无线网络中基于模糊逻辑的分级垂直切换算法

在异构无线网络中,针对综合考虑网络端和用户端参数的垂直切换算法,参数权重难以确定,同时基于模糊逻辑的垂直切换算法存在复杂度高的问题,该文提出一种基于模糊逻辑的分级垂直切换算法。首先,将接收信号强度(RSS)、带宽、时延输入到1级模糊逻辑系统,结合规则自适应匹配,推理出QoS模糊值,并通过QoS模糊值对网络进行初步筛选得到候选网络集;然后通过触发机制触发2级模糊逻辑系统,并将候选网络的QoS模糊值、网络负载率、用户接入费用输入2级模糊逻辑系统,同时结合规则自适应匹配,得到输出判决值,从而选择最佳接入网络。最后,实验结果表明,该算法能保证网络性能的同时,降低系统的时间开销。     

车辆异构网络中预测垂直切换算法

在车辆异构网络中,针对垂直切换决策时刻之后网络状态的动态变化,提高切换性能问题,提出一种基于马尔可夫过程的预测垂直切换(M-VHO)算法。算法考虑了切换决策后网络状态的动态变化对车辆终端服务质量(QoS)的影响。其基本思路是:在需要垂直切换时,利用马尔可夫过程的转移概率预测未来网络状态的变化;另外,采用模糊逻辑方法确定评价属性参数权重;最后,比较切换决策、切换执行和切换之后时刻的总收益来优化选择最佳切换网络。仿真结果证明,该算法在确保较高负载均衡的情况下,可有效改进车辆终端的平均阻塞率及丢包率,降低乒乓效应,确保了车辆终端的QoS。     

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

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。     

Heterogeneous Wireless Networks: Configuration and Vertical Handoff Management

Vertical handoff is one of the most important issues for the next generation heterogeneous wireless networks. However, in many situations, unbeneficial vertical handoffs occur across intersystem heterogeneous networks cause network performance degradation. Therefore, we propose a novel configuration architecture that can be deployed in the next generation of wireless networks. Second, we propose a predictive and adaptive Vertical Handoff Decision Scheme that optimizes the handoff initiation time as well as selection of the most optimal network. The proposed vertical handoff decision algorithm considers the technology type as well as the Signal to Interference Ratio (SIR), the Mobile Station (MS) velocity, the user preferences, the applications requirements and the terminal capabilities as the most important factors to make vertical handoff decision. In order to minimize handoff costs, the proposed decision algorithm uses the dwell timer concept. The handoff costs are analyzed in terms of unnecessary and unbeneficial handoffs rate.The simulation results show that the reduction of unnecessary handoffs proposed in our vertical handoff decision scheme reduces the handoff blocking probability, the packets loss rate and the handoff overhead     

Joint admission control algorithm based on load transfer in heterogeneous networks

A load-transfer-based joint admission control (LJAC) algorithm in heterogeneous networks was proposed.The access requirements of users were admitted based on load balancing,the dynamic load transfer of traffics in the overlapping coverage areas of heterogeneous networks were introduced,and the influence of such factors as the layout of heterogeneous networks and the vertical handoff was considered in the algorithm.The integrated system of heterogeneous networks was modeled as a multidimensional Markov chain,the steady-state probabilities were obtained and the quality of service (QoS) performance metrics were derived.Based on the Poisson point process theory,the upper bound of capacity of the heterogeneous networks satisfying QoS limitations was obtained.The admission control parameters of the integrated system of heterogeneous networks were optimized in order to maximize the resource utilization rate as well as guaranteeing the QoS of users.The simulation results demonstrate lower traffic blocking probability,lower failure probability of vertical handoff requirements,and larger system capacity gain can be achieved by using the proposed LJAC algorithm.     

Artificial Neural Network Based Vertical Handoff Algorithm for Reducing Handoff Latency

One of the most challenging topics for next generation wireless networks is vertical handoff concept since several wireless technologies are assumed to cooperate. Plenty of parameters related to user preferences, application requirements, and network conditions, such as; data rate, service cost, network latency, speed of mobile, battery level, interference ratio and etc. must be considered in vertical handoff process along with traditional RSSI information. In this study, a new artificial neural network based handoff decision algorithm is proposed in order to reduce the handoff latency of smart terminal deployed in aforementioned wireless heterogeneous infrastructures. The prominent parameters data rate, monetary cost and RSSI information are taken as inputs of the developed vertical handoff decision system. Performance results of the proposed system are also compared with those of classical Multiple Attribute Decision Making method Simple Additive Weighting, and of some other artificial intelligence based algorithms. According to the results obtained, the proposed neural network based vertical handoff decision algorithm is able to determine whether a handoff is necessary or not properly, and selects the best candidate access network considering the abovementioned parameters. The results also show that, the neural network based algorithm developed significantly reduces the handoff latency while the number of handoffs, which is another vital performance metric, is still reasonable.     

异构无线网络资源管理与切换控制技术

随着通信技术的发展,新型业务逐渐增多,传统的通信网络无法满足各种业务之间巨大的性能需求差异,未来的通信网络逐渐向异构网络进行发展。针对异构网络中资源调配与垂直切换的问题,利用虚拟网络技术与资源虚拟化技术设计了异构网络中的无线资源管理方法,并设计了子网间垂直切换算法。该算法利用层次分析法与熵权法得到各网络参数的综合权值,加权得到各网络的效用值并依此选择最优网络。通过搭建系统平台验证了设计的异构无线网络的有效性,为后续异构无线网络的研究提供了参考。     

基于分段式PPP协商的快速网络接入算法

多网融合是当今无线通信网络发展的必然趋势,垂直切换是网络融合的关键技术之一。对现有3G网络的终端接入信令流程进行分析,提出了一种基于分段式PPP协商的快速接入算法。通过在现网环境中测试,证明该算法有效地缩短了切换执行时延,对垂直切换过程中"切换执行"阶段的研究做出了贡献。     

超密集异构无线网络中基于位置预测的切换算法

在密集异构蜂窝网络和无线局域网络构成的超密集异构无线网络中, 变速移动的车辆终端会面临更加频繁的切换,导致用户服务质量(QoS)变差。该文针对上述问题,首先,利用高斯马尔可夫移动模型,预测车辆下一时刻的位置,筛选出满足终端服务质量的候选网络集,与当前的候选网络集做交运算,其次,当前接入网络不在交集中,则使用变步长的萤火虫算法寻找最佳网络;再次,对因预测误差导致的切换失效,则把终端用户迁移到宏蜂窝,以保证通信的持续性。仿真结果表明,在超密集异构无线网络中,使用该文所提算法能够减少乒乓切换等频繁切换现象,同时,提升了用户的服务质量和网络吞吐量。     

无线异构网络中带有动态权值的新型垂直切换算法

In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS factors. In this paper, we develop a novel vertical handoff algorithm in an integrated 3G cellular and Wireless LAN networks. The proposed algorithm can adjust the weight of each QoS attribute dynamically as the networks change, trace the network condition and choose the optimal access point at transient regions. Simulation results show that this algorithm is able to provide accurate handoff decision, resulting in small unnecessary handoff numbers, good performance of throughput and handoff delay in heterogeneous environments.     

Seamless vertical handoff using modified weed optimization algorithm for heterogeneous wireless networks

In the global scenario, a variety of wireless access networks are available. Different types of applications such as real time, nonreal time, and high bandwidth availability are used for heterogeneous wireless networks. Therefore, it is necessary for a service provider to make an appropriate connection support. For better performance, connections are to be exchanged among the different networks using seamless vertical handoff (VHO). The proposed algorithm shows the effect of optimization technique, which involves handoff decision process using vertical handoff triggering and selection of the network. The handoff triggering is initiated by using the received signal strength (RSS). In traditional method, handoff triggering is initiated by using RSS only. This method, modified weed optimization (M-WO) algorithm, reduces the unnecessary handoff by considering both RSS and velocity of the mobile node in handoff triggering. The parameters such as battery lifetime, handoff call dropping rate, load, dynamic weights adaptation and so on are to be considered individually or combined to make an effective network selection process. This paper highlights a novel effect ofM-WOalgorithm for decision making during the VHO. Our effort is to essentially optimize the system load, so that it reduces the handoff call dropping rate and the battery power consumption of the mobile node (MN). Weight of each QoS metrics is adjusted along with the networks changing conditions to trace the M-WO. Therefore, the novel VHO decision-making algorithm is superior to the existing SSF and OPTG methods. The simulation results show that the performance ofM-WOalgorithm is far better than SSF andOPTGmethods in terms of load, handoff call dropping rate and battery lifetime of MN.