NeTS: Small: Optimized Mobile Data Off-loading Architectures and Mechanisms
NeTS: Small: Optimized Mobile Data Off-loading Architectures and Mechanisms
批准号:
1527090
负责人:
Leandros Tassiulas
金额:
$48.76万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-10-01 至 2018-09-30
中文摘要
移动数据流量的空前增长,预计到2020年将超过25艾字节/月,这给移动运营商带来了重大的技术和经济挑战,并且需要快速增加容量。一些基于技术升级和频谱获取的网络扩展方法是昂贵且更长期的方法。我们需要替代的解决方案,而移动数据传输到Wi-Fi网络似乎是最有希望的解决方案之一。尽管最近有一些举措,但研究界仍然缺乏系统的方法来实现Wi-Fi网络的无缝集成,并确保最大可能的卸载效益。该项目将通过提出、分析和验证技术经济框架来填补这一空白,从而实现优化和高效的移动数据卸载解决方案。首先,它将分析运营商如何部署Wi-Fi覆盖网络(大规模、长期优化),以及如何使用它以有效的方式卸载蜂窝流量(动态、实时优化)。然后,它将研究运营商如何通过租赁用户拥有的休眠网络资源(如住宅Wi-Fi接入点)以及与Wi-Fi共享社区或Wi-Fi运营商合作来外包卸载。这些方法不仅可以实现现场和按需卸载,还可以促进可持续的网络解决方案,并在这个新兴的移动生态系统中为新的商业模式创造机会。提出的方案是网络优化和网络经济学的联系,并结合博弈论和拍卖理论工具,采用凸、随机和离散优化。具体而言,该项目包括以下两个重点:(I) Wi-Fi接入点部署、定价策略和流量分流政策,以及(II) Wi-Fi备用容量市场和机制。我将研究在蜂窝网络中覆盖Wi-Fi辅助网络的网络策略。这些政策将与向卸载移动数据的用户收费的定价方案联合设计。这种方法与目前免费卸载服务的做法有很大的不同,预计将为移动网络运营商带来显著的经济效益。此外,该项目将研究动态(在线)策略,以适应流量变化,并针对给定的网络基础设施优化卸载效益,同时考虑到用户?要求。该推力的理论分析涉及具有挑战性的优化问题,这些问题通常是np困难的,需要复杂和/或轻量级的解决方案算法。第二项研究将研究运营商如何有效地租用休眠用户拥有的Wi-Fi网络以进行卸载。本研究涉及分散的架构,因为网络基础设施由不同的实体拥有,并且通常涉及用户发起的卸载方案,其中移动用户决定何时卸载其流量。根据租赁网络是由独立的个人用户拥有,还是由Wi-Fi运营商拥有,还是由Wi-Fi共享社区拥有,将分析各种场景。每种情况都提出了独特的问题,因为所需的卸载机制和激励设计问题都是不同的。更广泛的影响:该项目受到电信行业当前挑战的推动,建立在真实数据集的基础上,并得到广泛的试验台实验的支持。该研究将通信系统设计、网络优化和网络经济学结合起来,将对科学、教育和通信市场产生广泛影响。首先,它将通过优化Wi-Fi网络和卸载机制大幅增加蜂窝容量。这将是适应不断增长的移动数据流量的决定性一步。它还可以激发新的商业模式,以及蜂窝运营商、Wi-Fi运营商和Wi-Fi共享社区之间的协作方案。此外,该项目采用了一种激进的方法,将共享经济模式应用于无线网络,有望引领可持续的网络解决方案。此外,该项目将成为学术界和工业界之间的一个良好渠道,积极参与顶级研究实验室,如阿尔卡特朗讯贝尔实验室。将采用一流的网络试验台设施,以建立真实的模型并评估拟议的体系结构。这将创建可被研究界重用的数据集,用于研究下一代异构无线网络的各个方面;这也将作为进一步传播研究成果的一种方法。将特别强调为该项目招募妇女和代表性不足的少数民族学生,以及通过本科生研究经验(reu)招募本科生。将系统地开展外展活动,以识别、吸引和培养STEM领域的杰出人才。
英文摘要
The unprecedented growth of mobile data traffic, that is expected to surpass 25 exabytes/month in 2020, poses major technical and economic challenges to the cellular operators, and necessitates rapid capacity increase. Several network expansion methods based on technology upgrades and spectrum acquisitions, are costly and more long-term approaches. There is a need for alternative solutions, and mobile data offloading to Wi-Fi networks appears as one of the most promising. Despite several recent initiatives, the research community still lacks a systematic approach for achieving a seamless integration of Wi-Fi networks and for ensuring the maximum possible offloading benefits. This project will fill this gap by proposing, analyzing, and validating a techno-economic framework that enables optimized and efficient mobile data offloading solutions. First, it will analyze how an operator can deploy a Wi-Fi overlay network (large-scale, long-term optimization), and how it can be used to offload cellular traffic in an efficient fashion (dynamic, real-time optimization). Then, it will study how operators can outsource offloading by leasing dormant user-owned network resources, such as residential Wi-Fi access points, and by cooperating with Wi-Fi sharing communities or Wi-Fi operators. Such approaches not only enable on-the-spot and on-demand offloading, but also promote sustainable networking solutions and create opportunities for novel business models in this emerging mobile ecosystem.The proposed plan lies at the nexus of network optimization and network economics, and employs convex, stochastic and discrete optimization in conjunction with game theory and auction theory tools. In particular, the project comprises the following two Thrusts: (I) Wi-Fi Access Point Deployment, Pricing Strategies, and Traffic Offloading Policies, and (II) Wi-Fi Spare Capacity Markets and Mechanisms. Thrust I will study network policies for overlaying Wi-Fi auxiliary networks in cellular networks. These policies will be jointly designed with pricing schemes for charging the users who offload mobile data. This approach departs significantly from the current practice of gratis offloading services, and is expected to bring significant economic benefits for the mobile network operators (MNOs). Moreover, the project will study dynamic (online) policies that adapt on traffic variations and optimize, for a given network infrastructure, the offloading benefits while taking into account the users? requirements. The theoretical analysis in this Thrust involves challenging optimization problems, which are often NP-hard, and require sophisticated and/or lightweight solution algorithms. The second Thrust will study how the operators can effectively lease dormant user-owned Wi-Fi networks for offloading purposes. This study refers to decentralized architectures as the network infrastructure is owned by different entities, and often to user-initiated offloading schemes where the mobile users decide when to offload their traffic. Various scenarios will be analyzed based on whether the leased networks are owned by independent individual users, by Wi-Fi operators, or Wi-Fi sharing communities. Each case raises unique issues as both the required offloading mechanisms, and the incentive design problems are different. Broader Impacts: This project is motivated by current challenges of the telecommunications industry, builds upon real data sets, and is supported by extensive testbed experimentation. The proposed research, incorporating communication system design, network optimization, and network economics, will have broad impact on science, education, and communication markets. First, it will increase substantially the cellular capacity by optimized Wi-Fi networks and offloading mechanisms. This will be a decisive step towards accommodating the surging mobile data traffic. It can also inspire new business models, and collaboration schemes among cellular operators, Wi-Fi operators, and Wi-Fi sharing communities. Besides, this project adopts a radical approach for employing sharing economy models to wireless networks, which is expected to lead in sustainable networking solutions. Moreover, the project will serve as an excellent conduit between academia and industry, involving actively top research labs such as the Alcatel-Lucent Bell Labs. Top-notch network testbed facilities will be employed in order to build realistic models and evaluate the proposed architectures. This will create datasets that can be reused by the research community for studying various aspects of the next generation heterogeneous wireless networks; this will also serve as a method for further disseminating the research outcomes. Special emphasis will be given on recruiting women and under-represented minority (URM) students to the project, as well as undergraduate students via Research Experience for Undergraduates (REUs). A systematic effort will be made in outreach activities to identify, engage, and nurture exceptional talents for careers in STEM.
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