基于移动终端业务的多接入网络选择算法

本文提出了一种基于移动终端业务的多接入网络选择算法,该算法将网络选择的单位从终端转向业务,并通过如下三个步骤为业务确定一个最优接入网络集合：首先,通过充分考虑业务的实际QoS需求以及终端速度的影响,确定各决策因素的权重;其次,通过效用函数模型确定各接入网络的效用函数矩阵;最后,通过最优接入网络集确定适合业务传输的最优接入网络组合。仿真结果表明：本文提出的新算法能根据终端移动速度及业务需求为业务选择最优网络组合,同时能大幅度提高终端网络的吞吐量。     

多终端协同接入网络选择算法研究

针对"虚拟终端"中多个终端设备协同工作时的接入网络选择问题,介绍了提供虚拟终端功能的网络结构;研究了多终端协同工作时业务的提供方法;分析了多终端同时接入时网络选择参数的变化情况;通过对已有多属性决策算法的改进,提出了多终端协同的接入网络选择(CANS)算法,实现了接入网络选择结果具有整体最优化。仿真结果表明,协同接入网络选择算法与随机接入网络选择法相比,业务吞吐量得到显著提高。     

基于效用理论的异构无线网络选择

在未来的异构无线网络环境中,为满足数据业务的需求,移动用户总希望能够接入最适合的网络。针对将实际测量的指标值归一化后作为决策值中存在的问题,提出了将效用理论应用到多指标算法中,设计了指标在不同业务、终端状态、用户类型下的效用函数,并用指标效用替代归一化测量值作为决策值计算备选网络的总效用,最后根据总效用值的比较来选择最优网络。仿真表明,此算法能够更好地反映用户的需求,帮助用户选择适合的网络。     

基于传输速率自适应的动态带宽分配算法

提出一种基于传输速率自适应的动态带宽分配算法,为异构无线网络中的多业务提供服务质量保证。根据所提的传输速率优先级决策模型,在传输速率QoS需求和异构网络容量约束的条件下,通过动态调整不同网络中各个移动终端所支持业务的传输速率来得到最优带宽重分配矩阵,以最大化整个异构网络的效用函数;将自适应带宽重分配问题描述为一个优化问题,采用动态优化的迭代算法自适应调节用户传输速率来进一步最大化该效用函数。理论分析和数值仿真结果表明,所提算法在给定传输速率且满足QoS需求的基础上,能够最大化网络的效用函数并减小新呼叫的阻塞概率。     

多网络共存下基于贝叶斯的垂直切换算法

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。     

异构无线网络中多模终端多接入选择机制研究

为进一步提升多模移动终端的传输性能和服务连续性,设计了一种利用异构无线网络多无线传输分集进行多网络同时接入和并行传输的多模终端多接入选择机制,该机制应用指数平滑方法处理已测量参数;使用状态转移模型管理终端多个网卡的接入选择策略;采用效用函数动态平衡终端功耗与传输速率。仿真结果表明与传统切换式接入选择相比,所提出的方法显著减少了不必要的接入重选次数,并在保证终端功耗与吞吐量的前提下提高了传输能效。     

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。     

基于群决策的多业务无线接入选择方法

在异构无线环境中,为了给运行了多个业务(群呼叫)的移动终端选择一个合适的无线接入网络,提出了一种基于群决策的接入选择方法。该方法引入效用函数对网络QoS及功耗进行评价;根据业务特点分配各属性的模糊权重,并采用业务优先权来反映各业务在群决策中的相对重要性;基于模糊理论,设计合理的模糊TOPSIS数据聚合模型,并在效用评价基础上实现了群决策。仿真结果表明,该方法能有效地实现多业务的无线接入选择,并且业务优先权在很大程度上影响了群决策结果。     

基于群决策的多业务无线接入选择方法

在异构无线环境中,为了给运行了多个业务（群呼叫）的移动终端选择一个合适的无线接入网络,提出了一种基于群决策的接入选择方法。该方法引入效用函数对网络QoS及功耗进行评价;根据业务特点分配各属性的模糊权重,并采用业务优先权来反映各业务在群决策中的相对重要性;基于模糊理论,设计合理的模糊TOPSIS数据聚合模型,并在效用评价基础上实现了群决策。仿真结果表明,该方法能有效地实现多业务的无线接入选择,并且业务优先权在很大程度上影响了群决策结果。     

基于用户偏好的异构网络选择算法研究

针对当前大多数网络选择算法没有充分考虑用户基于业务的网络偏好这一问题,提出了一种基于用户偏好的异构网络选择算法。该算法充分考虑业务需求、用户偏好及网络性能差异,通过层次分析法和距离分析法分别计算用户对各候选网络的主观和客观偏好权重,根据用户策略动态确定综合权重,并由此结合各属性权重计算网络性能,选出最优网络。仿真结果表明该算法可以准确选择接入网络,并当用户在网络边缘做兵乓运动时有效减少切换次数。     

一种智能ABC支持型QoS切换决策机制

本文提出了一种总最佳连接ABC(Always Best Connected)支持型服务质量QoS(Quality of Service)切换决策机制,引入模糊数学和微观经济学等相关知识,刻画应用类型、QoS需求、接入网络和移动终端,综合考虑应用QoS需求、用户愿付费用、用户对接入网络编码制式偏好、用户对接入网络供应商偏...     

多业务无线网络中基于最佳等信干比的功率控制

功率控制技术是无线网络的关键技术之一。为改进多业务无线网络中基于非合作博弈的功率控制算法中纳什均衡的帕累托有效性,引入数据终端的最佳等信干比概念,使得系统中所有数据终端都工作在最佳的等信干比下,语音终端工作在语音通信服务质量门限的目标信干比门限之下。推导出一个新的分布式基于最佳等信干比的功率控制算法。仿真表明,该算法明显提高了系统的服务质量,系统中终端均具有相对较高的效用和较低的发射功率,还使得无线网络资源的使用更加合理和公平。     

A novel noncooperative game competing model using generalized simple additive weighting method to perform network selection in heterogeneous wireless networks

Network selection mechanisms have a significant role in guaranteeing the QoS for users in a heterogeneous wireless networks environment. These mechanisms allow the selection of an optimal wireless network to satisfy the needs of users. Users are provided with the opportunity to select from multiple connectivity opportunities available all over various wireless networks. Furthermore, the network operators themselves can execute active selection strategies that facilitate proper decision making, in which user preferences are considered. This study proposes a new noncooperative competing game‐theoretic model and strategy space based on user preference. This model can solve network selection problems and capture the inter‐linkages of decisions taken by various networks. A generalized simple additive weighting method is incorporated into the framework of noncooperative game theory. In addition, the utility function is employed to assess the usefulness of the system. Simulation results and analysis illustrate the efficacy of the suggested model in attaining optimum network utility for heterogeneous wireless networks while optimizing user satisfaction. Copyright © 2014 John Wiley & Sons, Ltd.     

Utility‐optimal cross‐layer resource allocation in distributed wireless cooperative networks

In this paper, we propose a distributed cross‐layer resource allocation algorithm for wireless cooperative networks based on a network utility maximization framework. The algorithm provides solutions to relay selections, flow pass probabilities, transmit rate, and power levels jointly with optimal congestion control and power control through balancing link and physical layers such that the network‐wide utility is optimized. Via dual decomposition and subgradient method, we solve the utility‐optimal resource allocation problem by subproblems in different layers of the protocol stack. Furthermore, by introducing a concept of pseudochannel gain, we model both the primal direct logical link and its corresponding cooperative transmission link as a single virtual direct logical link to simplify our network utility framework. Eventually, the algorithm determines its primal resource allocation levels by employing reverse‐engineering of the pseudochannel gain model. Numerical experiments show that the convergence of the proposed algorithm can be obtained and the performance of the optimized network can be improved significantly. Copyright © 2012 John Wiley & Sons, Ltd.     

Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner     

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。     

Network energy optimization and intelligent routing in WSN applicable for IoT using self-adaptive coyote optimization algorithm

The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self-adaptive meta-heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self-adaptive coyote optimization algorithm (SA-COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA-COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA-COA. From the simulation findings, the best value of the designed SA-COA-LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self-adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C-ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.     

Max–min utility fair flow management for networks with route diversity

A regional central manager is employed to set aside, for the regional (or back-bone) network that it manages, for each flow class, communications capacity resources for a specific future time horizon. In the context of such a traffic management operation, a longer temporal scale is involved in controlling the admission and distribution of flows across the network. For management scal-ability purposes, flows are aggregated into flow classes. Furthermore, we consider a network operation under which multiple simultaneously activated routes are employed, across possibly distinct segments, to distribute traffic between identified source–destination pairs. We aim to ensure that the utility assigned to each class is as high as feasibly possible while striving to raise the utility gained by all classes in a max–min fair manner. In doing so, we incorporate the communications capacity constraints that are imposed by the underlying hybrid of directional and/or multiple-access wireline and wireless communications media employed across the network system. We develop and present in this paper an optimal algorithm for solving such a traffic management problem. It yields multi-utility-based max–min fair distributions of flow rates, per each class, across the specified multitude of simultaneously activated multi-segment routes. To guarantee that admitted flows are granted their desired capacity resources (and targeted corresponding utility levels), the selection of optimal flow distributions across the network routes is combined with the use of a flow admission control scheme that serves to optimally limit the aggregate rate of flows admitted for each flow class. As illustrative examples, we demonstrate the effectiveness of our solution in comparing its performance with that obtained under the use of a traffic regulation scheme that is not overlaid with a traffic management mechanism that serves to set aside resources for the support of flow classes. We also illustrate the use of our optimal algorithm for determining the optimal placement of unmanned aerial vehicle platforms that serve to supplement a terrestrial transport segment with a space-based one. Copyright © 2010 John Wiley & Sons, Ltd.