Kriging-Based 3-D Spectrum Awareness for Radio Dynamic Zones Using Aerial Spectrum Sensors

TL;DR

This paper presents a 3D Kriging interpolation method using UAV-based spectrum sensors to create accurate radio maps within radio dynamic zones, enhancing spectrum awareness for interference prevention.

Contribution

It introduces a realistic 3D correlation model and a Kriging-based interpolation technique tailored for UAV measurements in radio dynamic zones.

Findings

3D Kriging significantly improves radio map accuracy

Propagation models account for air-ground path loss and shadowing

Method is sensitive to measurement density and distribution

Abstract

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 paper, we consider a 3D RDZ scenario and propose to use unmanned aerial vehicles (UAVs) equipped with spectrum sensors to create and maintain a 3D radio map of received signal power from different sources within the RDZ. In particular, we introduce a 3D Kriging interpolation technique that uses realistic 3D correlation models of the signal power extracted from extensive measurements carried out at the NSF AERPAW platform. Using C-Band signal measurements by a UAV at altitudes between 30 m-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 3D 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 3D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss.