NSF-BSF:CIF: Small: A Unified View of Estimation and Information Relationships for Networks and Beyond

NSF-BSF：CIF：小型：网络及其他领域的估计和信息关系的统一视图

基本信息

批准号：
1908308
负责人：
Harold Vincent Poor
金额：
$ 50万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908308&HistoricalAwards=false
关键词：
NSF BSF CIF Small Unified

项目摘要

The emerging internet of things (IoT) paradigm is predicted to support extremely large networks with a massive number of devices. Given the importance that IoT device are expected to play in the future communication infrastructure, it is important to understand the fundamental limits of performance of such large networks. Classically the performance limits of communications networks are studied by using a framework of information theory -- a science put forth by the pioneering engineer and scientist Claude Shannon. The goal of this research is to expand the set of tools needed for the analysis of future communication networks. Specifically, the aim is to bring necessary tools from estimation theory---a branch of statistics dealing with the recovery of noisy data. The proposed work will advance knowledge by developing a unified view of communication networks through the lens of information and estimation theories. In summary, the successful completion of the project is expected to contribute new mathematical and engineering tools, fundamentally new perspectives and models, in order to enable efficient design of the next generation of communication infrastructure.A specific goal of this research is to study information flow over large networks by using a network transform that maps a problem of information flow over an arbitrary network to a well-structured estimation theoretic problem. In other words, instead of analyzing the problem in the classical information theoretic domain, the proposed transform offers a new estimation theoretic domain for analyzing performance of communication networks. The key attractive feature of the proposed network transform is that the underlying estimation theoretic optimization problem is a tractable one. By providing new insights and tools for studying optimal information flow over the networks via estimation theoretic techniques, this research has the potential not only to advance theory but also to improve latency, energy efficiency and robustness of emerging IoT applications. This project is a collaborative effort between researchers in the US and Israel, with funding for Israeli researchers provided by the Bi-National Science Foundation (BSF).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.

预计新兴的物联网（IoT）范式将支持具有大量设备的极大网络。鉴于预期物联网设备将在未来的通信基础架构中发挥作用的重要性，因此重要的是要了解此类大型网络的性能的基本限制。经典地，通过使用信息理论的框架来研究通信网络的性能限制 - 开拓者工程师和科学家克劳德·香农（Claude Shannon）提出的科学。这项研究的目的是扩大分析未来通信网络所需的工具集。具体而言，目的是从估计理论中带来必要的工具 - 统计数据的分支，涉及噪声数据的恢复。拟议的工作将通过通过各种信息和估计理论来开发通信网络的统一观点来提高知识。总而言之，预计该项目的成功完成将为新的数学和工程工具提供新的数学和工程工具，从根本上是新的观点和模型，以便有效地设计下一代通信基础架构。这项研究的具体目标是通过将网络转换绘制出一个跨越的网络的网络转换来研究大型网络，从而将信息的问题流向良好的构建构建问题。换句话说，所提出的转换没有在经典信息理论领域中分析问题，而是提供了一个新的估计理论领域，用于分析通信网络的性能。提出的网络转换的主要吸引力是，基本估计理论优化问题是可处理的问题。通过提供新的见解和工具来通过估计理论技术研究网络上的最佳信息流，这项研究不仅有可能提高理论，还可以提高新兴物联网应用的潜伏期，能源效率和鲁棒性。该项目是美国和以色列研究人员之间的一项合作努力，由双国科学基金会（BSF）为以色列研究人员提供资金。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来通过评估来支持的。