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Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study
By: Shufang Li; Mingyan Liu; Qian Zhang; Dawei Chen; Sixing Yin;
2012 / IEEE
This item was taken from the IEEE Periodical ' Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study ' Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literatures calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper, we present a detailed spectrum measurement study, with data collected in the 20 MHz to 3 GHz spectrum band and at four locations concurrently in Guangdong province of China. We examine the statistics of the collected data, including channel vacancy statistics, channel utilization within each individual wireless service, and the spectral and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2D frequent pattern mining algorithm that can predict channel availability based on past observations with considerable accuracy.
Channel Vacancy Duration
Service Congestion Rate
Spectrum Usage Prediction
Frequent Pattern Mining
Computing And Processing
Frequency 20 Mhz To 3 Ghz
Spectrum Usage Data Mining
Large-scale Spectrum Measurement
Dynamic Spectrum Access
Channel Vacancy Statistics
2d Frequent Pattern Mining Algorithm