Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map

TL;DR

This paper introduces two innovative radiomap inpainting methods leveraging propagation priority and depth maps to accurately reconstruct missing spectrum data in restricted areas, enhancing network planning tools.

Contribution

It proposes novel inpainting approaches that incorporate radio propagation models and depth maps for effective reconstruction of incomplete radiomaps.

Findings

Propagation priority improves inpainting accuracy.

Depth map-based approach handles large missing areas.

Experimental results validate the effectiveness of the methods.

Abstract

Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and are important tools for network planning in modern wireless systems. Generally, radiomaps are constructed from radio strength measurements by deployed sensors and user devices. However, not all areas are accessible for radio measurements due to physical constraints and security consideration, leading to non-uniformly spaced measurements and blanks on a radiomap. In this work, we explore distribution of radio spectrum strengths in view of surrounding environments, and propose two radiomap inpainting approaches for the reconstruction of radiomaps that cover missing areas. Specifically, we first define a propagation-based priority and integrate exemplar-based inpainting with radio propagation model for fine-resolution small-size missing area reconstruction on a radiomap. Then, we introduce a novel radio depth map and propose a two-step template-perturbation approach for large-size restricted region inpainting. Our experimental results demonstrate the power of the proposed propagation priority and radio depth map in capturing the PSD distribution, as well as the efficacy of the proposed methods for radiomap reconstruction.