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.

新兴的物联网 (IoT) 范式预计将支持具有大量设备的超大型网络。鉴于物联网设备预计在未来通信基础设施中发挥的重要性，了解此类大型网络性能的基本限制非常重要。传统上，通信网络的性能限制是通过使用信息论框架来研究的，信息论是由先驱工程师和科学家克劳德·香农提出的一门科学。这项研究的目标是扩展分析未来通信网络所需的工具集。具体来说，目的是从估计理论中引入必要的工具——估计理论是处理噪声数据恢复的统计学分支。拟议的工作将通过信息和估计理论的视角开发通信网络的统一视图来增进知识。总之，该项目的成功完成预计将贡献新的数学和工程工具、全新的视角和模型，从而实现下一代通信基础设施的高效设计。本研究的一个具体目标是研究信息流通过使用网络变换将任意网络上的信息流问题映射到结构良好的估计理论问题。换句话说，所提出的变换不是分析经典信息论领域中的问题，而是为分析通信网络的性能提供了新的估计理论领域。所提出的网络变换的主要吸引人的特征是潜在的估计理论优化问题是一个易于处理的问题。通过提供新的见解和工具，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推进理论，而且有可能改善新兴物联网应用的延迟、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合作成果，由美国国家科学基金会 (BSF) 为以色列研究人员提供资金。该奖项反映了 NSF 的法定使命，并通过使用该基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。