CIF: Small:Toward a Stochastic Geometry for Cellular Systems

CIF：小：走向蜂窝系统的随机几何

• 批准号: 1525904
• 负责人: Martin Haenggi
• 金额: $ 49.26万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1525904&HistoricalAwards=false
• 关键词: CIF; Small; Toward; Stochastic; Geometry

Demands for wireless Internet and voice access have continued to grow exponentially, while the available spectrum remains scarce. As a result, novel architectures and transmission techniques are needed for cellular networks to improve their spectral efficiency and provide consistent and high-speed wireless service for all users. The two key approaches to achieve this goal are increased network density and heterogeneous network architectures, where multiple tiers of base stations are deployed with different capabilities, depending on the user density and traffic demands. For such networks, new mathematical models and techniques are needed that capture their inherent randomness and heterogeneity. Stochastic geometry is a mathematical theory that is ideally suited for such problems. It provides both the models and the theory for the analysis of the network performance and user experience. This project focuses on the development of stochastic geometry-based tools tailored to the fifth generation of cellular systems (5G), which will result in novel design insights and help identify promising network architectures without the need for extensive and expensive simulations. Hence it will have a significant impact on the discussions on 5G that currently dominate the wireless industry and academic research and may even influence the standardization process. In addition, the project devises novel analytical techniques and makes theoretical contributions that are applicable beyond cellular networks, and it helps train future generations of students in emerging wireless technologies and analysis techniques.As cellular networks become denser and more heterogeneous, the locations of the base stations become more irregular due to restrictions on the placement and adaptation to users and traffic. As a result, classical network models such as lattices become outdated and need to be replaced by models that capture the inherent randomness in the base station locations. Recently, researchers have applied techniques from stochastic geometry for the analysis of some of the key metrics of cellular systems, most notably the signal-to-interference ratio, which determines the quality of the wireless connections. However, the underlying model was mostly restricted to the Poisson point process, which is analytically convenient but not very realistic. The analysis of more accurate models and of advanced transmission schemes such as base station cooperation and multi-antenna transmission has proven rather difficult. Hence there is an urgent need to devise new models that accurately describe current and future cellular networks and to significantly extend the set of tools for their analysis. This proposal aims at meeting this need by applying novel ideas and recent insights to develop new theoretical methods that expand the currently available ones in three main directions: (1) efficient ways to obtain highly accurate approximate results for diverse network models; (2) fine-grained and sharp results on the experience of individual users; (3) fundamental insight into the impact of the temporal dependence of the interference in cellular systems. The analytical methods used include Palm theory, Tauberian theorems, series and factorial moment expansions, and general probability theory, and the models will be validated with actual data.

对无线互联网和语音接入的需求持续呈指数增长，而可用频谱仍然稀缺。因此，蜂窝网络需要新颖的架构和传输技术来提高频谱效率，并为所有用户提供一致的高速无线服务。实现这一目标的两个关键方法是增加网络密度和异构网络架构，其中根据用户密度和流量需求部署具有不同功能的多层基站。对于此类网络，需要新的数学模型和技术来捕获其固有的随机性和异质性。随机几何是一种非常适合解决此类问题的数学理论。它为网络性能和用户体验的分析提供了模型和理论。该项目重点开发针对第五代蜂窝系统（5G）的基于随机几何的工具，这将带来新颖的设计见解，并有助于识别有前途的网络架构，而无需进行广泛且昂贵的模拟。因此，它将对目前主导无线行业和学术研究的5G讨论产生重大影响，甚至可能影响标准化进程。 此外，该项目设计了新颖的分析技术，并做出了适用于蜂窝网络之外的理论贡献，有助于培训下一代学生新兴无线技术和分析技术。随着蜂窝网络变得更加密集和更加异构，基地的位置由于布局、适应用户和交通的限制，车站变得更加不规则。因此，诸如晶格之类的经典网络模型已经过时，需要被捕获基站位置固有随机性的模型所取代。最近，研究人员应用随机几何技术来分析蜂窝系统的一些关键指标，尤其是决定无线连接质量的信号干扰比。然而，底层模型大多局限于泊松点过程，分析方便但不太现实。事实证明，更精确的模型和基站协作、多天线传输等先进传输方案的分析相当困难。因此，迫切需要设计新的模型来准确描述当前和未来的蜂窝网络，并显着扩展用于分析的工具集。该提案旨在通过应用新颖的想法和最新的见解来开发新的理论方法来满足这一需求，这些方法将当前可用的方法扩展到三个主要方向：（1）为不同的网络模型获得高精度近似结果的有效方法； （2）对个人用户体验的细粒度、锐利的结果； (3) 对蜂窝系统中干扰的时间依赖性影响的基本洞察。所使用的分析方法包括棕榈理论、陶伯定理、级数和阶乘矩展开以及一般概率论，并且模型将用实际数据进行验证。