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。     

车联网中基于贝叶斯决策的垂直切换方法研究

车辆需要在车联网的异构无线网络环境下进行垂直切换,针对垂直切换技术普遍不能支持 WAVE, WiMAX和3G cellular间的垂直切换这一问题,提出了一种基于贝叶斯决策的垂直切换算法。首先,根据接入网络的信号强度、传输速率、误码率和网络阻塞率以及车辆终端的速度和运动趋势建立多条件相关的切换概率分布,计算出切换先验概率;然后通过贝叶斯决策算法计算后验概率并进行决策分类,从而选取最优网络接入。仿真实验结果表明,该算法不仅有效地实现WAVE,WiMAX和3G cellular无线接入技术之间的垂直切换,而且避免了乒乓效应,保证了网络及时更新。     

异构无线网络的自适应垂直切换判决算法

在未来的异构环境中,网络间的垂直切换将对QoS保证产生重要影响。针对移动终端在异构网络间切换不理想的问题,提出了一种自适应的垂直切换判决算法。采用基于用户多应用的代价函数对接入网络进行评估与选择,综合考虑移动终端当前的电池电量,判断当前业务是否需要进行网络切换,使移动终端能自适应地进行切换判决。仿真结果表明,该算法可以有效地延长移动终端的工作时间,减少乒乓效应,提高系统的切换性能,改善业务的QoS。     

基于认知自选择决策树的垂直切换方法研究

在异构无线网络环境下高速移动的终端用户需要随时随地接入网络,然而,在垂直切换过程中,由于终端用户对网络选择的偏好不同以及切换之后网络连接时间和状态难以得到保障,无法满足用户的切换需求。因此,提出了一种支持终端用户高速移动的基于认知自选择决策树的垂直切换方法。该方法首先根据网络属性和终端运动趋势建立相应的切换概率分布,然后根据用户偏好选择相应的决策树进行决策,最后通过对切换后终端的业务类型和运动状态进行反馈分析,提出了反馈认知决策方法。仿真结果表明,所提的方法不仅能够保证高速移动终端在异构无线网络下的切换质量,而且减少了不必要的切换,保证了网络的及时更新。     

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。     

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.     

基于模糊层次—熵权法的自适应垂直切换算法

针对当前异构网络中,采用多决策属性的垂直切换算法难以动态调整网络属性的权重值,从而无法满足用户服务质量(QoS)的需求问题,文章以接入最优的异构无线网络和保障用户QoS为目标,提出基于模糊层次—熵权法的自适应垂直切换算法.首先,根据用户对网络属性的不同要求,采用主观模糊层次赋权与客观熵权法结合的方式构建网络属性优化模型...     

一种基于QoS满意度评价的mSCTP垂直切换决策方法

提出一种基于QoS满意度评价的mSCTP垂直切换决策方法,扩展了mSCTP的性能估计、QoS评价和垂直切换决策功能,根据不同应用类型的特定QoS需求进行单独QoS满意度评价和决策,并选择能够提供足够QoS保障的最恰当网络作为切换目标网络。仿真结果表明,该方法能够根据QoS进行自适应的切换决策控制,有效减少切换次数和优化应用性能。     

超密集异构无线网络中基于移动轨迹预测的网络切换算法

随着5G技术的广泛应用，网络超密集化部署已成为必然趋势。超密集异构无线网络在实现网络高流量密度、高峰值速率性能的同时，给传统的网络切换算法带来了挑战，处于变速移动的终端会面临更频繁的切换问题，这将导致乒乓效应频率的显著提高，进而影响用户在网体验。针对上述问题，该文提出一种基于终端移动轨迹预测的网络切换算法，适用于各类型用户在高密度无线网络中的垂直切换和水平切换问题。首先，为了更高精度的移动轨迹预测，提出一种基于模糊核聚类和长短期记忆(LSTM)神经网络的预测方法，可以有效预测不同移动模式下用户终端的短时移动轨迹；之后，基于用户当前和预测位置，获取候选网络集合，通过候选集交运算法和指标阈值判断网络切换时机；当切换触发时，使用帝企鹅算法最优化网络选择。仿真结果表明，相比于其他类型的时间序列预测算法，该文提出的轨迹预测算法精度较高；同时相较对比算法，该文所提网络切换算法的切换次数适中，有效避免了乒乓效应，且提高了用户连接的网络质量。     

支持终端晃动的多光源可见光通信系统有效容量研究

作为下一代网络的重点候选技术,可见光通信(VLC)以其超高的传输速率有望提供严格的时延服务质量(QoS)保障。现有VLC研究大多假设终端在接收信号过程中始终保持垂直向上。然而,在实际VLC系统中,终端晃动将对接收端入射角产生较大的影响,从而对VLC的信道增益与传输速率产生影响。该文针对统计时延QoS约束下多光源VLC系统晃动终端的可达传输速率展开研究。首先,基于3维离散时间马尔可夫链,提出VLC终端3自由度晃动模型,从时间相关性的角度刻画了VLC终端的随机晃动过程;其次,将终端晃动下多光源VLC系统的矢量传输过程映射为马尔可夫式服务过程,基于有效容量理论,研究支持终端晃动的VLC系统在统计时延QoS约束下的可达传输速率;最后,通过仿真实验说明了终端晃动对VLC系统容量的影响并验证了所推导有效容量的准确性。     

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.     

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.     

MDP-Based Network Selection Scheme by Genetic Algorithm and Simulated Annealing for Vertical-Handover in Heterogeneous Wireless Networks

The hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. Markov processes are widely used in performance modelling of wireless and mobile communication systems. We address the problem of optimal wireless network selection during vertical handover, based on the received information, by embedding the decision problem in a Markov decision process (MDP) with genetic algorithm (GA), we use GA to find a set of optimal decisions that ensures the best trade-off between QoS based on their priority level. Then, we emerge improved genetic algorithm (IGA) with simulated annealing (SA) as leading methods for search and optimization problems in heterogeneous wireless networks. We formulate the vertical handoff decision problem as a MDP, with the objectives of maximizing the expected total reward and minimizing average number of handoffs. A reward function is constructed to assess the QoS during each connection, and the AHP method are applied in an iterative way, by which we can work out a stationary deterministic handoff decision policy. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithm that avoid the slow and massive computations. This paper compares the formulation and results of five recent optimization algorithms: artificial bee colony, GA, differential evolution, particle swarm optimization and hybrid of (GA–SA). Simulation results indicated that choosing the SA rules would minimize the cost function, and also that, the IGA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.     

改善用户体验的垂直切换算法

针对由超密集异构无线网络的超高动态性而引起掉话率不断增长的问题,并考虑到以往基于模糊逻辑相关垂直切换算法的较大时间开销,该文提出一种改善用户体验的垂直切换算法。首先利用5G核心访问和移动管理功能发现终端附近的所有候选网络,同时,利用自组织网络技术的环境感知能力,随时监测网络的运行状况,主动维护网络之间的邻居关系表。然后,引入动态模糊神经网络(DFNN)算法来执行切换判决,将获取到的网络参数数据作为该系统的输入,动态生成对垂直切换有效的规则库,经学习之后,计算得到输出判决值,从而为终端选择最佳接入网络。最后,仿真结果表明,该算法能够明显改善垂直切换过程中的掉话情况,降低切换失败概率。同时,与其他同类算法的时间消耗相比,该算法能够维持在一个较低的水平。     

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     

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。