权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: SWIFT: Enabling Intelligent Pinpoint Space-Spectrum Utilization and Sharing

合作研究：SWIFT：实现智能精确空间频谱利用和共享

基本信息

批准号：
2128659
负责人：
Youjian Liu
金额：
$ 55万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128659&HistoricalAwards=false
关键词：
Collaborative Research SWIFT Enabling Intelligent

项目摘要

Future generations of communication systems must support vast numbers of mobile devices that share the spectrum efficiently with different priorities. To meet such a demand, this project goes beyond the traditional techniques of multi-antenna communications and explores a novel paradigm of wireless design by considering the spatial characteristics of signals, in addition to spectrum. This research aims to increase data rates and enable efficient spectrum sharing that could benefit 5G and 6G communication systems, as well as legacy systems. It is increasingly evident that efficient and high-speed communication systems, which are part of the nation's critical infrastructure, help improve society's productivity and maintain the nation's competitive edge. The project will also help train the workforce for the high-tech communication industry.For achieving the above goal, the key tasks of the project are 1) to estimate and learn the possible spatial signal transmission paths between nodes that form the multipath network, 2) to perform cross-layer design of signal paths selection, coding, broadcast and multiaccess transmissions, 3) and to undertake an experimental verification of the multipath network using software defined radios. Machine learning assisted algorithms will be designed to learn the multipath networks by taking advantage of the repeated patterns. Optimization and coding will be employed to jointly select signal transmission directions and design transmit signals so that the spectrum can be efficiently shared. The expected outcomes are methods for highly effective utilization and sharing of spectrum via a dense reuse of frequency across space.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.

未来几代的通信系统必须支持大量的移动的设备，这些设备以不同的优先级有效地共享频谱。为了满足这种需求，本项目超越了传统的多天线通信技术，并通过考虑信号的空间特性，除了频谱，探索一种新的无线设计范式。这项研究旨在提高数据速率，实现高效的频谱共享，从而使5G和6 G通信系统以及传统系统受益。越来越明显的是，作为国家关键基础设施的一部分，高效和高速的通信系统有助于提高社会生产力和保持国家的竞争优势。为实现上述目标，本项目的关键任务是：1）估计和学习构成多径网络的节点之间可能的空间信号传输路径; 2）进行信号路径选择、编码、广播和多址传输的跨层设计，3）并使用软件定义的无线电来进行多径网络的实验验证。机器学习辅助算法将被设计为通过利用重复模式来学习多路径网络。将采用优化和编码来联合选择信号传输方向和设计发射信号，以便可以有效地共享频谱。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。