  Spectrum map construction, which is crucial in cognitive radio (CR) system,
visualizes the invisible space of the electromagnetic spectrum for
spectrum-resource management and allocation. Traditional reconstruction methods
are generally for two-dimensional (2D) spectrum map and driven by abundant
sampling data. In this paper, we propose a data-model-knowledge-driven
reconstruction scheme to construct the three-dimensional (3D) spectrum map
under multi-radiation source scenarios. We firstly design a maximum and minimum
path loss difference (MMPLD) clustering algorithm to detect the number of
radiation sources in a 3D space. Then, we develop a joint location-power
estimation method based on the heuristic population evolutionary optimization
algorithm. Considering the variation of electromagnetic environment, we
self-learn the path loss (PL) model based on the sampling data. Finally, the 3D
spectrum is reconstructed according to the self-learned PL model and the
extracted knowledge of radiation sources. Simulations show that the proposed 3D
spectrum map reconstruction scheme not only has splendid adaptability to the
environment, but also achieves high spectrum construction accuracy even when
the sampling rate is very low.
