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SpectraMap: Efficiently Constructing a Spatio-temporal RF Spectrum Occupancy Map

机译:SpectraMap:有效构建时空RF谱占用率图

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The RF spectrum is typically monitored from a single, or few, vantage points. A larger spatio-temporal view of spectrum occupancy, such as over a few weeks on a city-wide scale, would be beneficial for several applications, for example, spectrum inventory by regulators or spectrum monitoring by wireless carriers. However, achieving such a view requires a dense deployment of spectrum analyzers, both in space and time, which is prohibitively expensive. In this paper, we present a novel efficient approach to obtain an accurate extrapolated spatio-temporal view of spectrum occupancy. Our method uses RSSI measurements alone and does not require a-priori information of terrain, transmitter location, transmit power or path-loss model. We present our method as an algorithmic framework, called Spectra Map, which through targeted deployment of both static and mobile spectrum analyzers, gives a view of the spectrum occupancy over both time and space. We contrast SpectraMap's accuracy with that of Kriging (an accepted well performing method of RSSI spatial extrapolation) through simulations and present RSSI map construction savings achieved through actual deployment on a large university campus. Finally, we draw a theoretical distinction between SpectraMap and relevant contemporary solutions in the fields of space-time RSSI maps and spectrum management.
机译:RF频谱通常从单个或少数的有利点监测。频谱占用率的更大的时空视图,例如在城市范围内的几周内,对几个应用是有益的,例如,通过监管机构或无线运营商的频谱监测频谱清单。然而,实现这样的视图需要在空间和时间内密集地部署频谱分析仪,这在空间和时间上是昂贵的。在本文中,我们提出了一种新的有效方法来获得精确的外推的频谱占用时空视图。我们的方法单独使用RSSI测量,不需要地形,发射器位置,发射功率或路径损耗模型的先验信息。我们将我们的方法作为算法框架,称为Spectra Map,它通过静态和移动频谱分析仪的有针对性部署,给出了两个时间和空间的频谱占用视图。通过模拟,我们将SpectraDamap(RSSI空间推断的良好表现方法)的精度进行了刺激性的精度,并通过大学校园实际部署实现了RSSI地图施工节省。最后,我们在空间RSSI地图和频谱管理领域的谱和相关的当代解决方案之间绘制了理论区分。

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