Machine Learning-Based Primary Exclusive Region Update for Database-Driven Spectrum Sharing

In database-driven spectrum sharing, despite the spectrum sharing policy given by a database, harmful interference can occur between a primary user (PU) and a secondary user (SU) due to the unexpected propagation paths. In a previous study, a primary exclusive region (PER) centered at a PU, wherein the SUs are forbidden to use the spectrum, has been proposed. However, the PER figure that efficiently covers the regions where interference occurs, cannot be circular. In this paper, we propose a framework for updating the PER adaptively with machine learning, when interference occurs. The framework employs undersampling and oversampling schemes considering the propagation characteristics and shadow fading in order to solve an imbalanced data problem degrading estimation accuracy of appropriate shape of PER. Our simulation results demonstrate that the area of PER with the proposed framework is smaller by 54% than that of the fixed circular PER setting, and the proposed sampling scheme achieves a 1% interference probability with 21% fewer iterations and a 6% smaller area compared to the existing sampling schemes.