Local and Low-Cost White Space Detection
Local and Low-Cost White Space Detection
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DOI：
10.1109/icdcs.2017.292
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Ahmed Saeed;Khaled A. Harras;E. Zegura;M. Ammar
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文献类型：
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作者：
Ahmed Saeed;Khaled A. Harras;E. Zegura;M. Ammar
空白区域是电视频谱中已分配但不在本地使用的部分。如果能准确检测到，空白空间为高速无线通信提供了宝贵的新机会。我们提出了一种新的空白检测方法，该方法允许节点在本地操作，基于集中构建的模型，并且成本低，同时比最知名的方法检测更多的频谱机会。我们有两个想法。首先，我们证明了低成本的频谱监测硬件可以提供“足够好的”检测能力。其次，我们开发了结合本地测量信号特征和位置的模型，以更有效地检测空白空间的可用性。我们将这些想法整合到我们称为Waldo的完整系统的设计、实现和评估中。我们在美国亚特兰大市区的一台笔记本电脑上部署了waldo，覆盖面积为700平方公里。我们的研究结果表明，除了位置之外，使用信号特征可以将某些通道的检测精度提高10倍。我们还在Android智能手机上部署了Waldo，展示了有效利用智能手机资源进行实时空白检测的可行性。
White spaces are portions of the TV spectrum that are allocated but not used locally. Ifaccurately detected, white spaces offer a valuable new opportunity for highspeed wireless communications. We propose a new method for white space detection that allows a node to actlocally, based on a centrally constructed model, and at low cost, whiledetecting more spectrum opportunities than best known approaches. Weleverage two ideas. First, we demonstrate that low-cost spectrum monitoringhardware can offer "good enough" detection capabilities. Second, we develop amodel that combines locally-measured signal features and location to more efficiently detect white space availability. We incorporate these ideas into the design,implementation, and evaluation of a complete system we call Waldo. We deployWaldo on a laptop in the Atlanta metropolitan area in the US covering 700 km2. Our results show that usingsignal features, in addition to location, can improve detection accuracy by up to10x for some channels. We also deploy Waldo on an Android smartphone,demonstrating the feasibility of real-time white space detection with efficientuse of smartphone resources.