Several alternatives for more efficient spectrum management have been proposed over the last decade, resulting in new techniques for automatic wideband spectrum sensing. However, while spectrum sensing technology is important, understanding, using and taking actions on this data for better spectrum and network resource management is at least equally important. In this paper, we propose a system that is able to automatically detect wireless spectrum events from streaming spectrum sensing data, and enables the consumption of the events as they are produced, as a statistical report or on a per-query basis. The proposed system is referred to as spectrum streamer and is wireless technology agnostic, scalable, able to deliver actionable information to humans and machines and also enables application development by custom querying of the detected events.
Using stream analytics for developing an accurate and near real-time spectrum monitoring tool is an emerging research topic. In @cite_1 , the authors developed SpecInsight, a system that can very accurately schedule when, where and on which frequency band to sense to detect spectrum signals. Their system is guided by the statistical expectation that activity will be present in certain bands. Their paper draws very interesting conclusions based on extensive experimental evaluation in seven locations and a week's worth of continuous sensing at each. This work is the closest to the proposed spectrum streamer in that it focuses on streaming data. However, they use streaming data to automate the spectrum sensing process while we use streaming data to automate the sensing, detection and notifying reporting. The spectrum streamer could for instance use the output of SpecInsight to perform the detection on large portions of the electromagnetic spectrum.
@cite_1
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Spectrum sensing has been an active research area for the past two decades. Nonetheless, current spectrum sensing systems provide only coarse occupancy data. They lack information about the detailed signal patterns in each band and can easily miss fleeting signals like radar. This paper presents SpecInsight, a system for acquiring a detailed view of 4 GHz of spectrum in realtime. SpecInsight's design addresses the intrinsic conflict between the need to quickly scan a wide spectrum and the desire to obtain very detailed information about each band. Its key enabler is a learned database of signal patterns and a new scheduling algorithm that leverages these patterns to identify when to sample each band to maximize the probability of sensing active signals. SpecInsight is implemented using off-the-shelf USRP radios with only tens of MHz of instantaneous bandwidth, but it is able to sense 4 GHz of spectrum, and capture very low duty-cycle signals in the radar band. Using SpecInsight, we perform a large-scale study of the spectrum in 7 locations in the US that span major cities and suburban areas, and build a first-of-its-kind database of spectrum usage patterns.