Most existing studies of spectrum usage have been performed by actively sensing the energy levels in specific RF bands including cellular bands. In this paper, we provide a unique, complementary analysis of cellular primary usage by analyzing a dataset collected inside a cellular network operator. One of the key aspects of our dataset is its scale - it consists of data collected over three weeks at hundreds of base stations. We dissect this data along different dimensions to characterize and model primary usage as well as understand its temporal and spatial variations. Our analysis reveals several results that are relevant if Dynamic Spectrum Access (DSA) approaches are to be deployed for cellular frequency bands. For instance, we find that call durations show significant deviations from the often-used exponential distribution, which makes call-based modeling more complicated. We also show that a random walk process, which does not use call durations, can often be used for modeling the aggregate cell capacity. Furthermore, we highlight some applications of our results to improve secondary usage of licensed spectrum.
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