Collaborative Research: Heavy Traffic Limit Models and Control Analysis for Wireless Queuing Systems - incorporating Long-Range Dependence and Heavy Tails

合作研究：无线排队系统的大流量限制模型和控制分析 - 结合远程依赖和重尾

• 批准号: 0608663
• 负责人: Vladas Pipiras
• 金额: $ 4.34万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0608663&HistoricalAwards=false
• 关键词: Collaborative; Research; Heavy; Traffic; Limit

Buche, DMS-060608669Ghosh, DMS-0608634Pipiras, DMS-0608663 The investigators study the heavy traffic analysis inwireless systems under novel phenomena of long-range dependenceand heavy tails. These phenomena are characteristic of currentwireless systems driven by data-intensive applications such asmultimedia, WWW, and real-time interactions. The heavy trafficapproach is a powerful way to analyze queuing systems at nearcapacity, yielding complex, yet tractable limit models thatretain the essential features of the actual queuing system. Butmost of the existing literature on heavy traffic analysis,including wireless systems, is based on short-range dependent(Markovian-like) models and light tails. In the case of heavytails only, the focus is on extending the perturbed test functionmethod, based on the martingale problem, for showing weakconvergence to a queuing model expected to be driven by stableLevy motion. When long-range dependence characteristics are alsopresent, the focus is on developing a powerful alternativeapproach based on the Poisson random measure representation oftraffic processes. In this case, weak convergence to a queuingmodel driven by fractional Brownian motion is expected. Thepower control problem associated with wireless systems is studiedalong with the convergence analysis to the limit queue model. This is done for given controls, as stochastic control analysisfor stable Levy motion or fractional Brownian motion is currentlyundeveloped. The focus of the project is on analyzing wireless systemscharacterized by high capacity applications such as multimedia. Such systems are ever more relevant with an increasing number ofwireless users (personal and military) and their demand fordata-intensive applications. For example, military applicationsinclude the use of video in battlefield settings for conferencingand providing information to troops. Personal applicationsinclude movies, real-time interactions, and WWW data. Inparticular, extensions of a developed approach, the heavy trafficmethod, are used in the analysis. This approach supposes thatthe wireless system is near its capacity, which is the case inpractice where applications make high demands on a limitedbandwidth. This assumption allows obtaining good models thatretain the essential features of the actual system while beingtractable. The models are useful for obtaining an understandingof system characteristics that is needed for the design of anefficient wireless network. Furthermore, obtaining these modelsis an important first step toward developing stochastic controlmethods for these emerging applications to optimize resourceallocations (for example, power) to the queues for transmissions.

Buche，DMS-060608669 Ghosh，DMS-0608634 Pipiras，DMS-0608663 研究人员研究了在无线系统中的大流量分析下的新现象的长距离依赖性和重尾。 这些现象是由诸如多媒体、WWW和实时交互等数据密集型应用驱动的当前无线系统的特征。 重交通的方法是一个强大的方法来分析排队系统在接近容量，产生复杂的，但易于处理的限制模型，保留了实际排队系统的基本特征。 但是，现有的大部分文献中的重流量分析，包括无线系统，是基于短程相关（马尔可夫类）模型和轻尾。 在重尾的情况下，重点是扩展的扰动测试函数方法，基于鞅问题，表现出弱收敛的排队模型，预计将由stableLevy运动。 当长程相关特性也是目前，重点是开发一个强大的替代方法的基础上泊松随机测量表示的交通过程。 在这种情况下，弱收敛到一个分数布朗运动驱动的模型是预期的。 研究了无线系统中的功率控制问题，并对极限排队模型进行了收敛性分析。这是为给定的控制，作为稳定的Levy运动或分数布朗运动的随机控制分析是目前尚未开发。 该项目的重点是分析以多媒体等高容量应用为特征的无线系统。这样的系统与越来越多的无线用户（个人和军事）及其对数据密集型应用的需求更加相关。 例如，军事应用包括在战场环境中使用视频进行会议和向部队提供信息。 个人应用程序包括电影、实时交互和WWW数据。 特别是，扩展的开发方法，重交通方法，用于分析。 这种方法假设无线系统接近其容量，这是实际应用对有限带宽提出高要求的情况。 这种假设允许获得良好的模型，保留了实际系统的基本特征，同时易于处理。 该模型是有用的，为获得一个理解的系统特性，需要一个高效的无线网络的设计。 此外，获得这些模型是一个重要的第一步，为这些新兴的应用程序开发随机控制方法，以优化资源分配（例如，功率）的队列传输。