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

TL;DR

It is illustrated that matrix completion can outperform ordinary Kriging and that the simple Kriging and trans-Gaussian Kriging yield better results when the density of known measurements is lower.

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.

Figures (8)

• Figure 1: 3D sparse RSRP measurement dataset IEEEDataPort_2 used in this study. The measurements were collected by a UAV that aimed to follow a predefined zig-zag pattern at five different altitudes.
• Figure 2: Signal propagation model used in this study involves a line of sight (LOS) and a ground reflected path from a base station to a UAV. Pathloss depends on $d_\text{3D},r_\text{1},r_\text{2}, \theta_\text{l},$ and $\theta_\text{r}$.
• Figure 3: Correlation modeling in a 3D space considering the horizontal and vertical distances. The correlation model serves as an important characteristic for interpolation.
• Figure 4: Ways of choosing neighbors: (a) Fixed radius $R$, (b) Fixed number of training samples $N$ around an unknown point.
• Figure 5: Performance comparison of ordinary Kriging and matrix completion for $110$ m height dataset. The best configuration of matrix completion ($N=20$) shows gain of $\sim\!0.2$ dB. over the best configuration of ordinary Kriging ($R=200$ m).