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In this paper, the authors apply approximate and multiscale entropy metrics to spectrum occupancy data gathered during an extensive measurement campaign. They show that the presented methods can be successfully applied to search for and quantify structures of highly varying complexities and time scales. Although structures can be found they are unfortunately relatively complex and it does not appear to be easy to directly exploit them for dynamic spectrum access. They also discuss the major foreseen application areas for entropy-based analysis. These include increasing the reliability of spectrum sensing as well as validation of spectrum occupancy models. Finally, they highlight some results that should be taken into account in future work on spectrum sensing.
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