Recent spectrum measurements show that geo-location spectrum databases are inaccurate in Metropolitan areas by missing a large number of TV white spaces (TVWS). To counter this challenge, this paper introduces a crowd-augmented spectrum database, whose spatial resolution and accuracy are continuously and opportunistically augmented by the spectrum sensing data from the crowd of mobile users. To augment the accuracy of our database to a desired level, an iterative Bayesian decision framework is proposed, which coherently combines two major modules over multiple rounds, including (1) Bayesian spatial prediction for optimal prediction of the power spectrum density (PSD) values at different spatial locations under parameter uncertainty and (2) Bayesian experimental design for efficient selection of the locations with high utility for additional sampling. The proposed framework is implemented and verified within our university main campus. Abundant spectrum opportunities are discovered, compared with the traditional geo-location databases, e.g., Google spectrum database.

最近的频谱测量结果表明，由于缺少大量电视空白（TVWS），大都会地区的地理位置频谱数据库不准确。为了应对这一挑战，本文介绍了一个人群增强频谱数据库，该空间数据库的空间分辨率和准确性通过来自移动用户人群的频谱感应数据而不断地，适度地得到了提高。为了将我们的数据库的准确性提高到所需水平，提出了一个迭代贝叶斯决策框架，该框架在多个回合中将两个主要模块紧密结合在一起，包括（1）贝叶斯空间预测，用于在参数不确定性下优化预测不同空间位置处的功率谱密度（PSD）值，以及（2）贝叶斯实验设计，用于有效选择位置，并具有较高的附加采样效用。拟议的框架在我们的大学主校区中得到实施和验证。与传统的地理位置数据库（例如Google频谱数据库）相比，发现了大量频谱机会。