CIF: Small: Deep Stochastic Geometry: A New Paradigm for Wireless Network Analysis and Design

CIF：小：深度随机几何：无线网络分析和设计的新范式

基本信息

批准号：
2007498
负责人：
Martin Haenggi
金额：
$ 50万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007498&HistoricalAwards=false
关键词：
CIF Small Deep Stochastic Geometry

项目摘要

The use of wireless networks is rapidly extending towards applications with strict reliability and/or latency constraints. For example, in 5G cellular systems, standards not only specify average data rates but also the minimum rates that 95% of the users should be able to achieve. Meanwhile, for vehicular safety messaging or in manufacturing, extremely high reliability under strict latency constraints is required. In contrast to these developments, the theoretical tools available for wireless network analysis and design mostly focus on network-wide averages, which makes them unsuitable for these new applications. As a result, there is an urgent need to develop a theory for networks with strict performance constraints and guarantees. This project focuses on the development of such a theory, which will allow a sharp performance analysis and enable researchers and engineers in industry to characterize the user experience much more efficiently than by lengthy and expensive simulations. Accordingly, it is expected to have a significant impact on the design of future wireless systems. In addition, it will help train future generations of students in emerging wireless technologies and analytical techniques.In view of the increasing density, irregularity, and uncertainty in the locations of wireless transceivers, a probabilistic approach to modeling and analysis that includes the network geometry as its key ingredient is warranted. Stochastic geometry is the natural mathematical tool for modeling and analysis. However, its use has been largely restricted to the derivation of average performance metrics, which do not capture the disparity in the link or user performances nor incorporate reliability or latency constraints. To address these shortcomings, this project develops a new theoretical framework, called deep stochastic geometry, that focuses on spatial distributions rather than merely averages. Deep stochastic geometry enables a direct evaluation of the performance of user or link percentiles and the performance under constraints. As such, it is a theory of guaranteed performance, in contrast to the existing theory of average performance. At the heart of the new theory are so-called meta distributions, which are distributions of conditional distributions (given the network geometry). Meta distributions naturally emerge when the different sources of randomness in a network are separated according to their time scales. The specific research activities include the development of efficient numerical methods and simulation techniques to calculate meta distributions, finding effective approximation techniques, the extension of meta distributions to joint distributions, and, finally, the combination of multiple metrics into a comprehensive approach to characterize and optimize network performance under constraints.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

无线网络的使用正在迅速扩展到具有严格可靠性和/或延迟约束的应用程序。例如，在5G蜂窝系统中，标准不仅指定了平均数据速率，还指定了95％的用户应达到的最低率。同时，对于车辆安全消息或制造业，需要在严格的延迟限制下可靠性。与这些发展相反，可用于无线网络分析和设计的理论工具主要集中于整个网络平均值，这使得它们不适合这些新应用程序。结果，迫切需要为具有严格绩效限制和保证的网络发展理论。该项目的重点是这种理论的发展，该理论将允许敏锐的性能分析，并使行业的研究人员和工程师能够比长期且昂贵的模拟更有效地表征用户体验。因此，预计将对未来无线系统的设计产生重大影响。此外，它将有助于培训新兴的无线技术和分析技术的未来学生。在无线收发器位置的密度，不规则性和不确定性的增加，一种概率的建模和分析方法，包括网络几何学在内，包括其关键成分。随机几何形状是用于建模和分析的自然数学工具。但是，它的使用在很大程度上仅限于平均性能指标的推导，这些指标不会捕获链接或用户性能中的差异，也不会纳入可靠性或延迟约束。为了解决这些缺点，该项目开发了一个新的理论框架，称为“深层随机几何形状”，该框架着重于空间分布，而不仅仅是平均。深层随机几何形状可以直接评估用户或链接百分位数的性能以及在约束下的性能。因此，与现有的平均绩效理论相反，这是保证绩效的理论。新理论的核心是所谓的元分布，它们是条件分布的分布（给定网络几何形状）。当网络中不同的随机性根据其时间尺度分离时，元分布会自然出现。具体的研究活动包括开发有效的数值方法和仿真技术来计算元分布，找到有效的近似技术，将元分布扩展到联合分布向联合分布的扩展，最后，将多个指标组合为一种全面的方法，以一种在约束中表征和优化Network的范围来表征和优化NSF的范围，以反映NSF的范围，以反映NSF的范围，以反映了NSF的范围。影响审查标准。