A Framework for Analyzing Spectrum Characteristics in Large Spatio-temporal Scales
A Framework for Analyzing Spectrum Characteristics in Large Spatio-temporal Scales
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DOI：
10.1145/3300061.3345450
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发表时间：
2019-08
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影响因子：
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通讯作者：
Yijing Zeng;Varun Chandrasekaran;Suman Banerjee;Domenico Giustiniano
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作者：
Yijing Zeng;Varun Chandrasekaran;Suman Banerjee;Domenico Giustiniano
在先验知识很少的情况下了解频谱特征需要频率、空间和时间域中的细粒度频谱数据；收集如此多样化的测量结果会产生大量数据。对所得数据集的分析提出了独特的挑战；现有的方法是针对特定的频谱相关应用程序（apps）量身定制的，并且不足以处理如此大规模的数据。在本文中，我们设计了 BigSpec，这是一个通用框架，可以快速处理应用程序。关键思想是通过对保留信号特征的压缩数据进行广泛的计算来降低计算成本。遵循这一准则，我们为三个应用程序构建了解决方案，即能量检测、时空频谱估计和异常检测。选择这些应用程序是为了突出 BigSpec 的效率、可扩展性和可扩展性。为了评估 BigSpec 的性能，我们收集了一年内超过 1 TB 的频谱数据，涵盖 300MHz-4GHz，覆盖 400 平方公里。与基线和之前的工作相比，我们实现了 17 倍的运行时效率，亚线性而不是线性的运行时可扩展性，并将异常的定义扩展到不同的域（频率和时空）。我们还从数据中获得高层次的见解，为未来的频谱测量和数据分析提供宝贵的建议。
Understanding spectrum characteristics with little prior knowledge requires fine-grained spectrum data in the frequency, spatial, and temporal domains; gathering such a diverse set of measurements results in a large data volume. Analysis of the resulting dataset poses unique challenges; methods in the status quo are tailored for specific spectrum-related applications (apps), and are ill equipped to process data of this magnitude. In this paper, we design BigSpec, a general-purpose framework that allows for fast processing of apps. The key idea is to reduce computation costs by performing computation extensively on compressed data that preserves signal features. Adhering to this guideline, we build solutions for three apps, i.e., energy detection, spatio-temporal spectrum estimation, and anomaly detection. These apps were chosen to highlight BigSpec's efficiency, scalability, and extensibility. To evaluate BigSpec's performance, we collect more than 1 terabyte of spectrum data spanning a year, across 300MHz-4GHz, covering 400 km2. Compared with baselines and prior works, we achieve 17× run time efficiency, sublinear rather than linear run time scalability, and extend the definition of anomaly to different domains (frequency & spatio-temporal). We also obtain high-level insights from the data to provide valuable advice on future spectrum measurement and data analysis.