EAGER: Collaborative Research: Global RF Spectrum Opportunity Assessment

EAGER：协作研究：全球射频频谱机会评估

• 批准号: 1265266
• 负责人: Allen MacKenzie
• 金额: $ 12.45万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1265266&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Global; RF

In order to apply emerging technologies (e.g. dynamic spectrum sharing) to address the wireless spectrum shortage problem, there is a critical need to understand global RF spectrum usage trends. To accomplish this, a three-pronged approach is being pursued: 1) deployment of geographically dispersed, temporally coordinated RF spectrum observatories in multiple U.S. locations, and through international collaboration, in Finland. The spectrum observatories use a common platform generating a single RF spectrum measurement dataset. 2) Development of empirically validated, statistical models of spectrum utilization for different wireless application types based on this dataset. 3) Use of "big data" analytical techniques to mine the dataset to discover temporal and spectral correlations not obvious using traditional approaches. As the models and relationships are refined, they will enable temporal and spectral occupancy predictions to support spectrum sharing for various circumstances and wireless applications.The generation of a high-resolution, multi-location, multi-national spectrum usage dataset using a common, consistent measurement and storage approach is unique and allows direct, unambiguous comparisons of spectrum usage across geographies and demographics. The statistical models of spectrum utilization and the identified similarities and differences between regions and wireless services are unique and inform dynamic spectrum sharing research and related regulatory action with "real-world" data. Importantly, this is the first time that "big data" analytic approaches are being systematically applied to RF utilization data providing new insights motivating novel dynamic spectrum sharing approaches and improved spectrum efficiency.

为了应用新兴技术（例如动态频谱共享）来解决无线频谱短缺问题，迫切需要了解全球RF频谱使用趋势。为了实现这一目标，正在采取三管齐下的方法：1）在美国多个地点部署地理上分散的、时间上协调的射频频谱观测站，并通过国际合作在芬兰部署。频谱观测站使用通用平台生成单个RF频谱测量数据集。2)基于此数据集，开发经验验证的不同无线应用类型的频谱利用率统计模型。3)使用“大数据”分析技术来挖掘数据集，以发现使用传统方法不明显的时间和光谱相关性。随着模型和关系的完善，它们将能够实现时间和频谱占用预测，以支持各种情况和无线应用的频谱共享。使用通用、一致的测量和存储方法生成高分辨率、多位置、多国频谱使用数据集是独一无二的，可以直接、明确地比较不同地理位置和人口统计数据的频谱使用。频谱利用率的统计模型以及地区和无线服务之间的相似性和差异性是独一无二的，并为动态频谱共享研究和相关监管行动提供了“真实世界”数据。重要的是，这是第一次将“大数据”分析方法系统地应用于RF利用数据，提供了新的见解，激发了新的动态频谱共享方法和提高的频谱效率。