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Crowdsensing for Spectrum Discovery: A Waze-Inspired Design via Smartphone Sensing
IEEE/ACM Transactions on Networking ( IF 3.0 ) Pub Date : 2020-03-10 , DOI: 10.1109/tnet.2020.2976927
Sen Lin , Junshan Zhang , Lei Ying

We study Waze-inspired spectrum discovery, where the cloud collects the spectrum sensing results from many smartphones and predicts location-specific spectrum availability based on information fusion. Observe that with limited sensing capability, each smartphone can sense only a limited number of channels; and further, the more channels each smartphone senses, the less accurate the sensing results would be. In particular, we consider two different smartphone sensing models: a homogeneous model and a heterogeneous model. To develop a comprehensive understanding, we cast the spectrum discovery problem as a matrix recovery problem, which is different from the classical matrix completion problem, in the sense that it suffices to determine only part of the matrix entries in the matrix recovery formulation. It is shown that the widely-used similarity-based collaborative filtering method would not work well because it requires each smartphone to sense too many channels. With this motivation, we propose a location-aided smartphone data fusion method and show that the channel numbers each smartphone needs to sense could be dramatically reduced. Moreover, we analyze the partial matrix recovery performance by using the location-aided data fusion method. Both theoretical analysis and numerical results corroborate the intuition that with each smartphone sensing more channels, the recovery performance improves at first but then degrades beyond some point because of the decreasing sensing accuracy.

中文翻译:

频谱发现的人群感知:通过智能手机感应进行位智启发的设计

我们研究Waze启发的频谱发现,其中云收集了许多智能手机的频谱感应结果,并基于信息融合预测了特定位置的频谱可用性。注意,由于感知能力有限,每个智能手机只能感知数量有限的频道。而且,每个智能手机感应到的频道越多,感应结果的准确性就越低。特别是,我们考虑了两种不同的智能手机感应模型:同质模型和异质模型。为了获得全面的了解,我们将频谱发现问题转换为矩阵恢复问题,这与经典的矩阵完成问题不同,因为它仅需要确定矩阵恢复公式中的部分矩阵项即可。结果表明,广泛使用的基于相似度的协作过滤方法无法很好地工作,因为它需要每个智能手机感应太多的渠道。以此动机为基础,我们提出了一种位置辅助的智能手机数据融合方法,并表明可以显着减少每个智能手机需要感知的频道数量。此外,我们通过使用位置辅助数据融合方法来分析部分矩阵的恢复性能。理论分析和数值结果都证实了这样的直觉,即随着每个智能手机检测到更多的通道,恢复性能首先会提高,但随后由于降低的检测精度而降低到某个点。我们提出了一种位置辅助的智能手机数据融合方法,并表明可以显着减少每个智能手机需要感知的通道号。此外,我们通过使用位置辅助数据融合方法来分析部分矩阵的恢复性能。理论分析和数值结果都证实了这样的直觉,即随着每个智能手机检测到更多的通道,恢复性能首先会提高,但随后由于降低了检测精度而降低到某个点。我们提出了一种位置辅助的智能手机数据融合方法,并表明可以显着减少每个智能手机需要感知的通道号。此外,我们通过使用位置辅助数据融合方法来分析部分矩阵的恢复性能。理论分析和数值结果都证实了这样的直觉,即随着每个智能手机检测到更多的通道,恢复性能首先会提高,但随后由于降低了检测精度而降低到某个点。
更新日期:2020-04-22
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