Issue No. 06 - June (2012 vol. 11)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2011.128
Sixing Yin , Beijing University of Posts and Telecommunications, Beijing
Dawei Chen , Hong Kong University of Science and Technology, Hong Kong
Qian Zhang , Hong Kong University of Science and Technology, Hong Kong
Mingyan Liu , University of Michigan, Ann Arbor
Shufang Li , Beijing University of Posts and Telecommunications, Beijing
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.
Spectrum measurement, channel vacancy duration, service congestion rate, spectrum usage prediction, frequent pattern mining, FPM-2D, spectral correlation, spatial correlation.
Sixing Yin, Dawei Chen, Qian Zhang, Mingyan Liu, Shufang Li, "Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study", IEEE Transactions on Mobile Computing, vol. 11, no. , pp. 1033-1046, June 2012, doi:10.1109/TMC.2011.128