EAGER: Collaborative Research: Global RF Spectrum Opportunity Assessment

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，迫切需要了解全球射频频谱使用趋势。为此，正在寻求一种三管齐下的方法：1)在美国多个地点部署地理上分散的、临时协调的射频频谱观测站，并通过国际合作在芬兰部署。频谱观测站使用一个公共平台来生成单一的射频频谱测量数据集。2)基于该数据集开发针对不同无线应用类型的经验证的频谱利用率统计模型。3)使用“大数据”分析技术挖掘数据集，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，它们将使时间和频谱占用预测能够支持各种环境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、多国家的频谱使用数据集是独一无二的，并允许直接、明确地比较不同地理和人口统计的频谱使用情况。频谱利用率的统计模型以及识别出的区域和无线服务之间的异同是独一无二的，并为动态频谱共享研究和相关监管行动提供“真实”数据。重要的是，这是“大数据”分析方法首次被系统地应用于射频利用率数据，提供了新的见解，激励了新的动态频谱共享方法和提高的频谱效率。

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.