EAGER: Collaborative Proposal: Global RF Spectrum Opportunity Assessment

为了应用新兴技术（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分散、临时协调的射频频谱观测站，并通过芬兰的国际合作。频谱观测站使用通用平台生成单个射频频谱测量数据集。2)基于该数据集开发经验验证的不同无线应用类型的频谱利用统计模型。3)利用“大数据”分析技术挖掘数据集，发现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、多地点、多国频谱使用数据集是独一无二的，可以直接、明确地比较不同地区和人口统计数据的频谱使用情况。频谱利用的统计模型以及确定的区域和无线业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“大数据”分析方法第一次被系统地应用于射频利用数据，提供了新的见解，激发了新的动态频谱共享方法，提高了频谱效率。

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.