3D Spectrum Awareness for Radio Dynamic Zones Using Kriging and Matrix Completion

TL;DR

This paper compares Kriging and matrix completion techniques for creating 3D radio environment maps in Radio Dynamic Zones, demonstrating that matrix completion often outperforms Kriging, especially with multi-altitude data and sparse measurements.

Contribution

The study introduces matrix completion for 3D spectrum awareness in RDZs, showing it surpasses Kriging and benefits from multi-altitude data integration.

Findings

Matrix completion outperforms Kriging in 3D radio map construction.

Using multi-altitude data improves prediction accuracy.

Lower measurement density favors simpler Kriging methods.

Abstract

Radio Dynamic Zones (RDZs) are geographically defined areas specifically allocated for testing new wireless technologies. It is essential to safeguard the regular spectrum users outside the zones from the interference caused by the deployed equipment within this zone. Previous works have utilized sparse reference signal received power (RSRP) measurements collected by unmanned aerial vehicles (UAVs) to construct a dense 3D radio map through ordinary Kriging. In this work, we illustrate that matrix completion can outperform ordinary Kriging. We partitioned a 2D area of interest into small square grids where each grid corresponds to a single entry of a matrix. The matrix completion algorithm learns the global structure of the radio environment map by leveraging the low-rank property of propagation maps. Additionally, we illustrate that the simple Kriging and trans-Gaussian Kriging yield better results when the density of known measurements is lower. Earlier works of RSRP prediction involved a training dataset at a single altitude. In this work, we also show that performance can be improved by utilizing a combined dataset from multiple altitudes.