共查询到18条相似文献,搜索用时 140 毫秒
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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。 相似文献
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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... 相似文献
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异构无线网络中基于SINR和层次分析法的SAW垂直切换算法研究 总被引:5,自引:0,他引:5
B3G或者4G无线网络体系致力于集成各种异构无线接入网络,其中一个主要的设计课题是支持垂直切换的研究。该文将多属性QoS考虑在内,针对各业务特点,提出一种基于SINR(信干噪比)和层次分析法(AHP)的SAW(简单加权法)垂直切换算法(SASAW)。它综合考虑SINR的影响以及要获得同等数据速率情况下,目标网络需要的等效SINR数值、用户通信代价、网络可用带宽等来构造属性矩阵做切换判决。为了评估性能,考虑3GPP定义的4类业务,利用层次分析法中的特征向量法来决定各个QoS属性之间的权重关系,构造比较判决矩阵并检验其一致性;根据判决矩阵,利用特征根法获得权重向量;最后根据属性矩阵和权重向量,利用SAW垂直切换算法进行判决。通过对算法的通过率、丢话率、垂直切换次数、平均用户代价等的性能比较,结果表明该文提出的算法能够根据各业务特点综合考虑各属性间关系,获得优良的系统性能。 相似文献
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针对目前的垂直切换技术普遍不能满足多网络下的切换问题,提出了一种改进的基于贝叶斯决策的垂直切换算法。首先根据接入终端网络的信号强度、网络负载、误码率和网络阻塞率以及用户业务对网络的实际需求偏好建立多条件相关的切换概率分布,计算出先验切换概率;然后通过贝叶斯决策算法计算出后验概率来进行判决比较,从而选取最优的接入网络。仿真结果表明,该算法不仅能有效地实现不同接入技术之间的垂直切换,从而减少不必要的切换,提高了切换成功的概率,而且还能在维持各网络平均负载达到30%~40%时减轻通信热点的阻塞情况,为用户提供更优的服务质量。 相似文献
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针对传统垂直切换算法中未能考虑能效这一关键因素,提出了一种能效最优的垂直切换算法,算法核心是移动终端检测并计算来至不同候选网络中的接收信号强度以及各候选小区中的系统容量,以候选小区中每单位能量所能提供的容量作为判决准则切入到最佳网络,同时结合传统的网络因子对多属性判决算法进行能效扩展。仿真结果表明,基于单位能量容量的切换算法较传统的切换算法节省10%左右的能量,扩展的多属性判决算法较传统的切换算法节省7%左右的能量,同时减少了“乒乓效应”,提高了用户的通信体验质量。 相似文献
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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。 相似文献
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摘要:针对车辆在异构网络覆盖环境下的网络接入选择问题,基于双边匹配博弈中稳定匹配的相关概念,提出一种车辆异构网络选择博弈模型,通过用户侧与网络侧的双向选择得到异构网络选择的稳定匹配。算法首先利用层次分析法获得相应权重分配以给出满意度函数,然后将网络侧的一对多匹配转化为一对一匹配问题,在稳定匹配条件下构建多目标优化模型,并求解最优匹配结果。相关性能分析及仿真实验表明,基于稳定匹配的博弈模型相对于传统决策模型,能够使网络和用户侧双方的满意度最优化,从而得到双向网络选择的均衡状态。 相似文献
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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。 相似文献
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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。 相似文献
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Network selection in heterogeneous access networks simultaneously satisfying user profile and QoS
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Javid Ali Raja Wasim Ahmad Tahir Maqsood Joel J. P. C. Rodrigues Nuhman Ul Haq Shahzad Sarwar Tassawar Iqbal Sajjad A. Madani 《International Journal of Communication Systems》2018,31(13)
Multimode capability empowers mobile devices to select the appropriate network to meet the requirements of user and applications. However, network selection is a challenging task owing to heterogeneous nature of network access links. At the same time, pervasiveness of mobile communication networks accompanied by the advances in wireless devices has raised the user expectations of persistent service and quality. Therefore, it is important for a mobile device to take situation‐based and timely decisions while selecting an access network to ensure both user's and operator's demand. The existing standard, called IEEE 802.21 (media‐independent handover), considers mobile device signal‐strength parameter for network selection from a list of networks. This research has proposed a ranking algorithm to rank heterogeneous networks based on a set of parameters including user profile and QoS. It selects most suitable network owing to multiple handover scenarios. The simulation results have shown that the proposed scheme has successfully satisfied apparently the contradictory requirements related to user profiles and QoS simultaneously. 相似文献
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针对可见光通信(VLC)与WiFi组成的异构网络中的网络选择问题,现有工作大多仅关注实际物理网络指标的优化,如信干噪比(SINR)和吞吐量等,而忽视了终端用户的异构需求和服务体验。基于此,该文提出了一种以用户体验质量(QoE)为优化目标的网络切换算法。该算法首先基于平均主观评分(MOS)机制建立离散化的QoE等级模型,然后根据不同网络对应的实际QoE水平给出了相应网络切换准则,当且仅当新网络使得用户QoE等级提升时才进行网络切换,从而在一定程度减小盲目追求容量优化引起的网络切换开销。仿真结果表明,该算法相比现有算法能够在保证QoE水平的前提下显著减小整体的网络切换次数,从而降低网络开销,提升网络性能。 相似文献
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Aditya Yadav Maushumi Barooah Sandip Chakraborty Sukumar Nandi 《Wireless Personal Communications》2014,77(1):507-527
Ensuring quality of service (QoS) for the mobile users during vertical handover between IEEE 802.11 wireless local area networks (WLAN) and data network provided by Ultramodern Telecommunication Systems (UMTS) is one of the key requirements for seamless mobility and transfer of existing connections from one network to another. QoS fulfillment is a complex problem and requires participation of both the mobile users as well as the connection networks. The QoS assurance criteria for existing connections can be affected by fluctuations of data rates when a user moves from the high speed WLAN network to the low speed UMTS network, even in the presence of another WLAN network in its vicinity. This can happen if the alternate WLAN network is highly loaded. Therefore handover from a high speed network to a low speed network should be avoided, whenever possible. This paper proposes a QoS based handover procedure that prioritizes the existing connection over the new connections so that rate fluctuations due to handover can be avoided if there exist another WLAN network in the range of the mobile user. Whenever the possibility of handover is detected, a pre-handover bandwidth reservation technique is used to reserve bandwidth at the alternate WLAN networks to avoid QoS degradation. The proposed scheme is implemented in Qualnet network simulator and the performance is analyzed and compared with traditional handover techniques. 相似文献
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Ali F. Almutairi Mohannad Hamed Mohamed Adnan Landolsi Mishal Algharabally 《Telecommunication Systems》2018,68(2):151-161
Mobile terminals can typically connect to multiple wireless networks which offer varying levels of suitability for different classes of service. Due to the changing dynamics of network attributes and mobile users’ traffic needs, vertical handovers across heterogeneous networks become highly desirable. Multiple attribute decision making (MADM) techniques offer an efficient approach for ranking competing networks and selecting the best one according to specific quality of service parameters. In this paper, a genetic algorithm (GA) is applied to optimize network attributes’ weighting by emphasizing ranking differences among candidate networks, thereby aiding correct decision making by reducing unnecessary handovers and ranking abnormalities. The performance of the proposed GA-based vertical handover is investigated with typical MADM techniques including Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results show that the proposed GA-based weight determination approach reduces the abnormality observed in the conventional SAW and TOPSIS techniques substantially. The results of this paper will help ensuring the application of MADM methods to more dynamic and challenging decision making problems encountered in wireless network. 相似文献
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Handover Management in Enhanced MIH Framework for Heterogeneous Wireless Networks Environment 总被引:1,自引:1,他引:0
Ying Wang Ping Zhang Yun Zhou Jun Yuan Fang Liu Gen Li 《Wireless Personal Communications》2010,52(3):615-636
Vertical handover decision making is one of the key problems in heterogeneous networks environment. In IEEE 802.21 standard,
a Media Independent Handover (MIH) framework is proposed to improve user experience of mobile devices by facilitating handover
in heterogeneous networks with measurements and triggers from link layers. However, vertical handover decision making can
benefit from the information more than link layers. In this paper, an Enhanced Media Independent Handover (EMIH) framework
is proposed by integrating more information from application layers, user context and network context. Given such information,
there is also another important problem on how to select a favorite network. Two quite important problems from realistic scenario
are as follows: (1) how to make use of partial knowledge due to incomplete value measurement on decision factors; (2) how
to deal with robustness problem due to inaccurate measurement on decision factors. In order to tackle these problems, two
novel Weighted Markov Chain (WMC) approaches based on rank aggregation are proposed in this paper, in which a favorite network is selected as the top one of rank aggregation result fused from multiple ranking lists based on decision factors. Moreover,
an entropy weighting method, combined with WMC approach, is studied. The simulations demonstrate the effectiveness of these
proposed approaches. 相似文献