2024 journal article
Kriging-Based 3-D Spectrum Awareness for Radio Dynamic Zones Using Aerial Spectrum Sensors
IEEE Sensors Journal.
TL;DR:
3-D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss, and the root-mean-square error of the signal power prediction achieved by the proposed 3-D Kriging method is notably lower compared to that of the perfect path loss-based prediction.
(via Semantic Scholar)
arxiv.org (repository)
Source: ORCID
Added: February 1, 2024
Radio dynamic zones (RDZs) are geographical areas within which dedicated spectrum resources are monitored and controlled to enable the development and testing of new spectrum technologies. Real-time spectrum awareness within an RDZ is critical for preventing interference with nearby incumbent users of the spectrum. In this article, we consider a 3-D RDZ scenario and propose to use unmanned aerial vehicles (UAVs) equipped with spectrum sensors to create and maintain a 3-D radio map of received signal power from different sources within the RDZ. In particular, we introduce a 3-D Kriging interpolation technique that uses realistic 3-D correlation models of the signal power extracted from extensive measurements carried out at the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) platform. Using ${C}$ -band signal measurements by a UAV at altitudes between 30 and 110 m, we first develop realistic propagation models on air-to-ground path loss, shadowing, spatial correlation, and semi-variogram, while taking into account the knowledge of antenna radiation patterns and ground reflection. Subsequently, we generate a 3-D radio map of a signal source within the RDZ using the Kriging interpolation and evaluate its sensitivity to the number of measurements used and their spatial distribution. Our results show that the proposed 3-D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss. Specifically, the root-mean-square error (RMSE) of the signal power prediction achieved by our proposed 3-D Kriging method is notably lower compared to that of the perfect path loss-based prediction, especially when the height difference between measured and the target locations is less than 20 m.
