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CRII: NeTS: Next Generation Spectrum Measurement Algorithms and Infrastructures

CRII：NeTS：下一代频谱测量算法和基础设施

基本信息

批准号：
1657476
负责人：
Mariya Zheleva
金额：
$ 17.5万
依托单位：
SUNY at Albany
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-02-15 至 2020-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657476&HistoricalAwards=false
关键词：
CRII NeTS Next Generation Spectrum

项目摘要

The current paradigm of exclusive spectrum allocation is creating artificial scarcity of spectrum resources that has a dramatic impact on network performance and user experience. Underutilized bands provide an opportunity for more efficient, shared spectrum access that has recently brought together policymakers, industry leaders and academic researchers to set an agenda for next-generation spectrum management. A critical enabler of such advances is deep understanding of spectrum use. This award will support research seeking to impact the scientific community, the policy domain and the public, by providing methods and informing system designs for efficient spectrum measurement and characterization, and informing the design of future mobile wireless networks and spectrum policy. Products from this research will be included in the PI's undergraduate and graduate courses, demonstrating the positive policy and societal impact of computer science and promoting the discipline to diverse groups of students.The goal of this project is to employ fundamental knowledge in signal processing and propagation, machine learning, and large-scale measurement to devise fast and robust algorithms and inform spectrum measurement infrastructure design. Of main research interest, will be the tight-knit mutual dependence between algorithms and infrastructures. The project will achieve this goal in two research tasks: (i) design and development of spectrum measurement algorithms for adaptive spectrum sensing, spectrum data management and spectrum characterization; and (ii) evaluation of spectrum measurement infrastructures that will illuminate the impact of spectrum sensor properties on spectrum characterization quality and will inform the design of mixed sensor cost/mobility measurement infrastructures. This research will bridge the gap between algorithm design and infrastructures. It will introduce the first holistic framework for spectrum measurement and characterization to facilitate next generation spectrum management. The design of adaptive, data-driven algorithms will facilitate rapid and efficient spectrum measurement and characterization. Exploration of the cost and mobility trade-offs in spectrum sensing infrastructure will inform efficient end-to-end infrastructure design that minimizes cost while maximizing the learning outcomes of spectrum sensing.

当前的独占频谱分配模式正在造成频谱资源的人为稀缺，这对网络性能和用户体验产生了巨大影响。未充分利用的频段为更高效的共享频谱接入提供了机会，最近政策制定者、行业领导者和学术研究人员聚集在一起，为下一代频谱管理制定了议程。这种进步的一个关键推动因素是对频谱使用的深刻理解。该奖项将通过提供有效频谱测量和表征的方法和通知系统设计，以及通知未来移动的无线网络和频谱政策的设计，支持寻求影响科学界，政策领域和公众的研究。该研究成果将被纳入PI的本科和研究生课程，展示计算机科学的积极政策和社会影响，并向不同的学生群体推广该学科。该项目的目标是利用信号处理和传播，机器学习和大规模测量的基础知识来设计快速和强大的算法，并为频谱测量基础设施设计提供信息。主要的研究兴趣，将是算法和基础设施之间紧密的相互依赖。该项目将通过两项研究任务实现这一目标：（i）设计和开发用于自适应频谱感知、频谱数据管理和频谱表征的频谱测量算法;（ii）评估频谱测量基础设施，以阐明频谱传感器特性对频谱表征质量的影响，并为混合传感器成本/移动性测量基础设施的设计提供信息。这项研究将弥合算法设计和基础设施之间的差距。它将引入第一个频谱测量和表征的整体框架，以促进下一代频谱管理。自适应数据驱动算法的设计将促进快速有效的光谱测量和表征。探索频谱感知基础设施中的成本和移动性权衡将为高效的端到端基础设施设计提供信息，该设计将最大限度地降低成本，同时最大限度地提高频谱感知的学习成果。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

AirVIEW: Unsupervised transmitter detection for next generation spectrum sensing

DOI：
10.1109/infocom.2018.8485946
发表时间：
2018
期刊：
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications
影响因子：
0
作者：
M. Zheleva;Petko Bogdanov;Timothy LaRock;Paul Schmitt
通讯作者：
M. Zheleva;Petko Bogdanov;Timothy LaRock;Paul Schmitt

Enabling a Nationwide Radio Frequency Inventory Using the Spectrum Observatory

使用频谱观测站启用全国范围的无线电频率清单

DOI：
10.1109/tmc.2017.2716936
发表时间：
2017
期刊：
IEEE Transactions on Mobile Computing
影响因子：
7.9
作者：
Zheleva, Zhivkova Mariya;Chandra, Ranveer;Chowdhery, Aakanksha;Garnett, Paul;Gupta, Anoop;Kapoor, Ashish;Valerio, Matt
通讯作者：
Valerio, Matt

Robust and Efficient Modulation Recognition Based on Local Sequential IQ Features