Measurement-calibrated conflict graphs for dynamic spectrum distribution
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Building accurate interference maps is critical for performing reliable and efficient spectrum allocation. In this work, we use empirical data to explore the feasibility of using measurement-calibrated propagation models to build accurate interference models. Our work shows that calibrated propagation models generate location-dependent signal prediction errors. Such error pattern leads to conservative conflict graphs that actually improve the reliability of spectrum allocations by reducing the impact of unpredicted accumulative interference.
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